{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78bed409",
   "metadata": {},
   "source": [
    "# How to customize a synapse\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_building/how_to_customze_a_synapse.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_building/how_to_customze_a_synapse.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "id": "4524939e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:48.992552Z",
     "start_time": "2025-10-06T03:56:45.166873Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "8659beb0c1dd5ae5",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88463aaf44fc77d",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### ``brainpy.dyn.ProjAlignPostMg2``\n",
    "\n",
    "![](../_static/align_post.png)\n",
    "\n",
    "Synaptic projection which defines the synaptic computation with the dimension of postsynaptic neuron group.\n",
    "\n",
    "```\n",
    "brainpy.dyn.ProjAlignPostMg2(\n",
    "   pre, \n",
    "   delay, \n",
    "   comm, \n",
    "   syn, \n",
    "   out, \n",
    "   post\n",
    ")\n",
    "```\n",
    "\n",
    "- ``pre (JointType[DynamicalSystem, AutoDelaySupp])``: The pre-synaptic neuron group.\n",
    "- ``delay (Union[None, int, float])``: The synaptic delay.\n",
    "- ``comm (DynamicalSystem)``: The synaptic communication.\n",
    "- ``syn (ParamDescInit)``: The synaptic dynamics.\n",
    "- ``out (ParamDescInit)``: The synaptic output.\n",
    "- ``post (DynamicalSystem)`` The post-synaptic neuron group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf67ba2650777a4",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### ``brainpy.dyn.ProjAlignPreMg2``\n",
    "\n",
    "Synaptic projection which defines the synaptic computation with the dimension of presynaptic neuron group.\n",
    "\n",
    "```\n",
    "brainpy.dyn.ProjAlignPreMg2(\n",
    "   pre, \n",
    "   delay,\n",
    "   syn,  \n",
    "   comm, \n",
    "   out, \n",
    "   post\n",
    ")\n",
    "```\n",
    "\n",
    "- ``pre (JointType[DynamicalSystem, AutoDelaySupp])``: The pre-synaptic neuron group.\n",
    "- ``delay (Union[None, int, float])``: The synaptic delay.\n",
    "- ``syn (ParamDescInit)``: The synaptic dynamics.\n",
    "- ``comm (DynamicalSystem)``: The synaptic communication.\n",
    "- ``out (ParamDescInit)``: The synaptic output.\n",
    "- ``post (DynamicalSystem)`` The post-synaptic neuron group.\n",
    "\n",
    "\n",
    "![](../_static/align_pre.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768b4d215ba267e9",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Synapse dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1255abfe",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Exponential Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f13bbb",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The single exponential decay synapse model assumes the release of neurotransmitter, its diffusion across the cleft, the receptor binding, and channel opening all happen very quickly, so that the channels instantaneously jump from the closed to the open state. Therefore, its expression is given by\n",
    "\n",
    "$$\n",
    "g_{\\mathrm{syn}}(t)=\\bar{g}_{\\mathrm{syn}} e^{-\\left(t-t_{0}\\right) / \\tau}\n",
    "$$\n",
    "\n",
    "where $\\tau$ is the time constant, $t_0$ is the time of the pre-synaptic spike, $\\bar{g}_{\\mathrm{syn}}$ is the maximal conductance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f4c2ed",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The corresponding differential equation:\n",
    "\n",
    "$$\n",
    "\\frac{d g}{d t} = -\\frac{g}{\\tau_{decay}}+\\sum_{k} \\delta(t-t_{j}^{k}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbabc23857c3aae6",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Only Exponential synapse model can use the ``AlignPost``. \n"
   ]
  },
  {
   "cell_type": "code",
   "id": "d3d7e29a9c49f91b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.003410Z",
     "start_time": "2025-10-06T03:56:48.999235Z"
    }
   },
   "source": [
    "class Exponen(bp.dyn.SynDyn, bp.mixin.AlignPost):\n",
    "  def __init__(self, size, tau):\n",
    "    super().__init__(size)\n",
    "    \n",
    "    # parameters\n",
    "    self.tau = tau\n",
    "    \n",
    "    # variables\n",
    "    self.g = bm.Variable(bm.zeros(self.num))\n",
    "    \n",
    "    # integral\n",
    "    self.integral = bp.odeint(lambda g, t: -g/tau, method='exp_auto')  \n",
    "  \n",
    "  def update(self, pre_spike=None):\n",
    "    self.g.value = self.integral(g=self.g.value, t=bp.share['t'], dt=bp.share['dt'])\n",
    "    if pre_spike is not None:\n",
    "      self.add_current(pre_spike)\n",
    "    return self.g.value\n",
    "      \n",
    "  def add_current(self, x):  # specical for bp.mixin.AlignPost\n",
    "    self.g += x\n",
    "    \n",
    "  def return_info(self):\n",
    "    return self.g"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "ecdfa156568f6924",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### AMPA model\n",
    "\n",
    "AMAP synapse is described by a differential equation.\n",
    "\n",
    "$$\n",
    "\\frac {ds}{dt} = \\alpha [T] (1-s) - \\beta s\n",
    "$$\n",
    "\n",
    "Where $\\alpha [T]$ denotes the transition probability from state $(1-s)$ to state $(s)$; and $\\beta$ represents the transition probability of the other direction. $\\alpha=0.98$ is the binding constant. $\\beta=.18$ is the unbinding constant. $T=.5\\, mM$ is the neurotransmitter concentration, and has the duration of 0.5 ms."
   ]
  },
  {
   "cell_type": "code",
   "id": "851ed416270408f4",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.013816Z",
     "start_time": "2025-10-06T03:56:49.008726Z"
    }
   },
   "source": [
    "class AMPA(bp.dyn.SynDyn):\n",
    "  def __init__(self, size, alpha= 0.98, beta=0.18, T=0.5, T_dur=0.5):\n",
    "    super().__init__(size=size)\n",
    "\n",
    "    # parameters\n",
    "    self.alpha = alpha\n",
    "    self.beta = beta\n",
    "    self.T = T\n",
    "    self.T_duration = T_dur\n",
    "\n",
    "    # functions\n",
    "    self.integral = bp.odeint(method='exp_auto', f=self.dg)\n",
    "    \n",
    "    # variables\n",
    "    self.g = bm.Variable(bm.zeros(self.num))\n",
    "    self.spike_arrival_time = bm.Variable(bm.ones(self.num) * -1e7)\n",
    "\n",
    "  def dg(self, g, t, TT):\n",
    "    return self.alpha * TT * (1 - g) - self.beta * g\n",
    "  \n",
    "  def update(self, pre_spike):\n",
    "    self.spike_arrival_time.value = bm.where(pre_spike, bp.share['t'], self.spike_arrival_time)\n",
    "    TT = ((bp.share['t'] - self.spike_arrival_time) < self.T_duration) * self.T\n",
    "    self.g.value = self.integral(self.g, bp.share['t'], TT, bp.share['dt'])\n",
    "    return self.g.value\n",
    "\n",
    "  def return_info(self):\n",
    "    return self.g"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "eaea0b186e57de42",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Synapse outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4985ff0fd086a05a",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### COBA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4a7b11654a56b7",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Given the synaptic conductance, the COBA model outputs the post-synaptic current with\n",
    "\n",
    "$$\n",
    "I_{syn}(t) = g_{\\mathrm{syn}}(t) (E - V(t))\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "id": "eb406128",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.023560Z",
     "start_time": "2025-10-06T03:56:49.019248Z"
    }
   },
   "source": [
    "class COBA(bp.dyn.SynOut):\n",
    "  def __init__(self, E):\n",
    "    super().__init__()\n",
    "    self.E = E\n",
    "\n",
    "  def update(self, conductance, potential):\n",
    "    return conductance * (self.E - potential)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "50b9bcfa67818168",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### CUBA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9335f60fabfd8d7a",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Given the conductance, this model outputs the post-synaptic current with a identity function:\n",
    "\n",
    "$$\n",
    "I_{\\mathrm{syn}}(t) = g_{\\mathrm{syn}}(t)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "id": "8a8a402925da67bc",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.033622Z",
     "start_time": "2025-10-06T03:56:49.029266Z"
    }
   },
   "source": [
    "class CUBA(bp.dyn.SynOut):\n",
    "  def __init__(self, E):\n",
    "    super().__init__()\n",
    "    self.E = E\n",
    "\n",
    "  def update(self, conductance, potential):\n",
    "    return conductance"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "6327c26bde8d386d",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Mg Blocking\n",
    "\n",
    "The voltage dependence is due to the blocking of the pore of the NMDA receptor from the outside by a positively charged magnesium ion. The channel is nearly completely blocked at resting potential, but the magnesium block is relieved if the cell is depolarized. The fraction of channels $B(V)$ that are not blocked by magnesium can be fitted to\n",
    "\n",
    "$$\n",
    "B(V) = {1 \\over 1 + \\exp(-0.062V) [Mg^{2+}]_o/3.57}\n",
    "$$\n",
    "\n",
    "Here, $[{Mg}^{2+}]_{o}$ is the extracellular magnesium concentration, usually 1 mM. \n",
    "\n",
    "If we make the approximation that the magnesium block changes instantaneously with voltage and is independent of the gating of the channel, the net NMDA receptor-mediated synaptic current is given by\n",
    "\n",
    "$$\n",
    "I=\\bar{g}sB(V)(V-E)\n",
    "$$\n",
    "\n",
    "where $V(t)$ is the post-synaptic neuron potential, $E$ is the reversal potential."
   ]
  },
  {
   "cell_type": "code",
   "id": "2c08e3ad8ed026e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.045664Z",
     "start_time": "2025-10-06T03:56:49.040859Z"
    }
   },
   "source": [
    "class MgBlock(bp.dyn.SynOut):\n",
    "  def __init__(self, E= 0., cc_Mg= 1.2, alpha= 0.062, beta= 3.57):\n",
    "    super().__init__()\n",
    "    self.E = E\n",
    "    self.cc_Mg = cc_Mg\n",
    "    self.alpha = alpha\n",
    "    self.beta = beta\n",
    "\n",
    "  def update(self, conductance, potential):\n",
    "    return conductance * (self.E - potential) / (1 + self.cc_Mg / self.beta * bm.exp(-self.alpha * potential))"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "e09e5789072f0846",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Synaptic communications"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6115e9f3a1c1d64f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Masked matrix\n",
    "\n",
    "![](../_static/masked_matrix.png)"
   ]
  },
  {
   "cell_type": "code",
   "id": "c2efd9ae1e402bbc",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.055993Z",
     "start_time": "2025-10-06T03:56:49.050914Z"
    }
   },
   "source": [
    "class MaskedLinear(bp.dnn.Layer):\n",
    "  def __init__(self, conn, weight):\n",
    "    super().__init__()\n",
    "    \n",
    "    # connection and weight\n",
    "    weight = bp.init.parameter(weight, (conn.pre_num, conn.post_num))\n",
    "    if isinstance(self.mode, bm.TrainingMode):\n",
    "      weight = bm.TrainVar(weight)\n",
    "    self.weight = weight\n",
    "\n",
    "    # connection\n",
    "    self.conn = conn\n",
    "    self.mask = bm.sharding.partition(self.conn.require('conn_mat'))\n",
    "\n",
    "  def update(self, x):\n",
    "    return x @ (self.weight * self.mask)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "108e21e42c5e19a8",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "code",
   "id": "298227f42e890e6b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.067811Z",
     "start_time": "2025-10-06T03:56:49.061372Z"
    }
   },
   "source": [
    "class SimpleNet(bp.DynSysGroup):\n",
    "  def __init__(self, proj, g_max=1.):\n",
    "    super().__init__()\n",
    "    \n",
    "    self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))\n",
    "    self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,\n",
    "                              V_initializer=bp.init.Constant(-60.))\n",
    "    self.syn = proj(self.pre, self.post, delay=None, prob=1., g_max=g_max)\n",
    "    \n",
    "  def update(self):\n",
    "    self.pre()\n",
    "    self.syn()\n",
    "    self.post()\n",
    "    \n",
    "    # monitor the following variables\n",
    "    conductance = self.syn.proj.refs['syn'].g\n",
    "    current = self.post.sum_inputs(self.post.V)\n",
    "    return conductance, current, self.post.V"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "7f790eede434802c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.086793Z",
     "start_time": "2025-10-06T03:56:49.081074Z"
    }
   },
   "source": [
    "def run_a_net(net):\n",
    "  indices = np.arange(1000)  # 100 ms\n",
    "  conductances, currents, potentials = bm.for_loop(net.step_run, indices, progress_bar=True)\n",
    "  \n",
    "  # --- similar to: \n",
    "  # runner = bp.DSRunner(net)\n",
    "  # conductances, currents, potentials = runner.run(100.)\n",
    "  \n",
    "  ts = indices * bm.get_dt()\n",
    "  fig, gs = bp.visualize.get_figure(1, 3, 3.5, 4)\n",
    "  fig.add_subplot(gs[0, 0])\n",
    "  plt.plot(ts, conductances)\n",
    "  plt.title('Syn conductance')\n",
    "  fig.add_subplot(gs[0, 1])\n",
    "  plt.plot(ts, currents)\n",
    "  plt.title('Syn current')\n",
    "  fig.add_subplot(gs[0, 2])\n",
    "  plt.plot(ts, potentials, label='Post V')\n",
    "  plt.legend()\n",
    "  plt.show()"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "d4805b34860bf8d6",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Exponential COBA"
   ]
  },
  {
   "cell_type": "code",
   "id": "cb832774a9f3149f",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:49.116320Z",
     "start_time": "2025-10-06T03:56:49.111364Z"
    }
   },
   "source": [
    "class ExponCOBA(bp.Projection):\n",
    "  def __init__(self, pre, post, delay, prob, g_max, tau=5., E=0.):\n",
    "    super().__init__()\n",
    "    \n",
    "    self.proj = bp.dyn.ProjAlignPostMg2(\n",
    "      pre=pre, \n",
    "      delay=delay, \n",
    "      comm=MaskedLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),\n",
    "      syn=Exponen.desc(post.num, tau=tau),\n",
    "      out=COBA.desc(E=E),\n",
    "      post=post, \n",
    "    )"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "b9e5a228eac6815c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:50.188956Z",
     "start_time": "2025-10-06T03:56:49.144212Z"
    }
   },
   "source": [
    "run_a_net(SimpleNet(ExponCOBA))"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\mixin.py:399: UserWarning: Please use \".sum_current_inputs()\" instead. \".sum_inputs()\" will be removed.\n",
      "  warnings.warn('Please use \".sum_current_inputs()\" instead. \".sum_inputs()\" will be removed.', UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "25beca74e9164b7bbb07fff5cbca8e4a"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x350 with 3 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLsAAAFpCAYAAAB0yCp4AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAA2J5JREFUeJzs3Xd8FHX6B/DPbE/d9EoghBZ6CQgRKWoUGzYsZ6NYUA8s4J2KZz8V7Nz54+TkBPTUw66oWJGiEIogTTpJCCW9163z+2N3ZneTTbKb7O7szvd5v177emmy2f3uMDsz32ee5/lyPM/zIIQQQgghhBBCCCFEBhRSD4AQQgghhBBCCCGEEF+hYBchhBBCCCGEEEIIkQ0KdhFCCCGEEEIIIYQQ2aBgFyGEEEIIIYQQQgiRDQp2EUIIIYQQQgghhBDZoGAXIYQQQgghhBBCCJENCnYRQgghhBBCCCGEENmgYBchhBBCCCGEEEIIkQ0KdhFCCCGEEEIIIYQQ2aBgFyESmT17NjIzM6UeBiGEEEIIIYQQIisU7CJ+tX//flx33XXo06cPdDod0tPTcdFFF+GNN96Qemiyt3XrVjz99NOora2VeiiEECIZOg/537/+9S+sXr1a6mEQQgghhIg4nud5qQdB5Gnr1q04//zz0bt3b8yaNQspKSk4deoUtm3bhhMnTuD48eNSD1FSs2fPxsaNG1FUVOSX13/llVfw17/+FYWFhZRBRghhEp2HAmPYsGFISEjAxo0bpR4KIYQQQggAQCX1AIh8Pf/889Dr9di5cydiYmJcfldeXi7NoAghhDCDxfNQc3MzwsPD2/3cbDbDarVCo9FIMCpCCCGEkMCiMkbiNydOnMDQoUPbTTAAICkpSfzvKVOmYOTIkW5fY9CgQZg2bRoAoKioCBzH4ZVXXsFbb72Ffv36QavVYty4cdi5c6dHY6qtrcWCBQuQmZkJrVaLXr16YebMmaisrBSfU15ejjvuuAPJycnQ6XQYOXIk3nnnHZfX8XYsX3zxBYYNGwadTodhw4bh888/b/ecjRs3guO4dnfGhfdqWyJy+PBh3HDDDUhMTERYWBgGDRqEv/3tbwCAp59+Gn/9618BAH379gXHceA4TswiW7VqFS644AIkJSVBq9ViyJAhePPNN9uNKTMzE1dccQV+/fVXnHPOOdDpdMjKysK7777brW1rMBjw1FNPoX///tBqtcjIyMDDDz8Mg8HQ7vUIIaSnQvE8tHr1apfjtcDdOWLq1KkYNmwYdu3ahcmTJyM8PByPPfaYyziXLl0qjvPgwYMAbOeP6667DnFxcdDpdBg7dizWrl3r8n7COLZs2YKFCxciMTERERERuOaaa1BRUSE+LzMzE3/88Qc2bdoknmumTp3q0bYghBBCCPEXyuwiftOnTx/k5+fjwIEDGDZsWIfPu+2223DXXXe1e97OnTtx9OhRPP744y7P/+CDD9DQ0IC7774bHMfhpZdewrXXXouCggKo1eoO36exsRGTJk3CoUOHcPvtt2PMmDGorKzE2rVrcfr0aSQkJKClpQVTp07F8ePHMX/+fPTt2xcff/wxZs+ejdraWjzwwANej+WHH37AjBkzMGTIECxevBhVVVWYM2cOevXq1Z3NCgDYt28fJk2aBLVajblz5yIzMxMnTpzAV199heeffx7XXnstjh49iv/97394/fXXkZCQAABITEwEALz55psYOnQorrzySqhUKnz11Vf485//DKvVinnz5rm81/Hjx3HdddfhjjvuwKxZs7By5UrMnj0bOTk5GDp0qMfb1mq14sorr8Svv/6KuXPnYvDgwdi/fz9ef/11HD16FF988UW3twchhLgTiuchb1VVVeHSSy/Fn/70J9x6661ITk4Wf7dq1Sq0trZi7ty50Gq1iIuLwx9//IGJEyciPT0djz76KCIiIvDRRx/h6quvxqeffoprrrnG5fXvu+8+xMbG4qmnnkJRURGWLl2K+fPn48MPPwQALF26FPfddx8iIyPFGy7OYyCEEEIIkQRPiJ/88MMPvFKp5JVKJZ+bm8s//PDD/Pfff88bjUaX59XW1vI6nY5/5JFHXH5+//338xEREXxjYyPP8zxfWFjIA+Dj4+P56upq8XlffvklD4D/6quvOh3Pk08+yQPgP/vss3a/s1qtPM/z/NKlS3kA/HvvvSf+zmg08rm5uXxkZCRfX1/v9VhGjRrFp6am8rW1tS7bBgDfp08f8WcbNmzgAfAbNmxwGZvwXqtWrRJ/NnnyZD4qKoo/efKk28/B8zz/8ssv8wD4wsLCdp+3ubm53c+mTZvGZ2VlufysT58+PAB+8+bN4s/Ky8t5rVbLP/TQQ+LPPNm2//3vf3mFQsH/8ssvLr9fvnw5D4DfsmVLu78lhJCeCMXz0KpVq9weu92dI6ZMmcID4JcvX+7yXGGc0dHRfHl5ucvvLrzwQn748OF8a2ury3ufe+65/IABA8SfCePIy8tzObcsWLCAVyqVLue0oUOH8lOmTOn0sxNCCCGEBBKVMRK/ueiii5Cfn48rr7wSe/fuxUsvvYRp06YhPT3dpVxCr9fjqquuwv/+9z/w9vUSLBYLPvzwQ1x99dWIiIhwed0bb7wRsbGx4v9PmjQJAFBQUNDpeD799FOMHDmy3V1rAOA4DgCwbt06pKSk4KabbhJ/p1arcf/996OxsRGbNm3yaiwlJSXYs2cPZs2aBb1e77JthgwZ0ul4O1JRUYHNmzfj9ttvR+/evd1+jq6EhYWJ/11XV4fKykpMmTIFBQUFqKurc3nukCFDxM8F2LLDBg0a5LK9Pdm2H3/8MQYPHozs7GxUVlaKjwsuuAAAsGHDBo/GTgghngrF85C3tFot5syZ4/Z3M2bMEDN6AaC6uho///wzbrjhBjQ0NIjH4aqqKkybNg3Hjh3DmTNnXF5j7ty5LmObNGkSLBYLTp482a3xEkIIIYQEAgW7iF+NGzcOn332GWpqarBjxw4sWrQIDQ0NuO6668TeIQAwc+ZMFBcX45dffgEA/PTTTygrK8Ntt93W7jXbBniECUdNTU2nYzlx4kSnZSwAcPLkSQwYMAAKhetXY/DgweLvvRmL8PwBAwa0e69BgwZ1OpaOCJOprj5LZ7Zs2YK8vDxEREQgJiYGiYmJeOyxxwCgXbCr7WcEbJ/TeXt7sm2PHTuGP/74A4mJiS6PgQMHApBvs2hCiLRC7TzkrfT09A6bzvft29fl/48fPw6e5/HEE0+0OxY/9dRTANofi7v7WQkhhBBCpEQ9u0hAaDQajBs3DuPGjcPAgQMxZ84cfPzxx+LF9bRp05CcnIz33nsPkydPxnvvvYeUlBTk5eW1ey2lUun2PYS78YHky7F0dFffYrF4/VqdOXHiBC688EJkZ2fjtddeQ0ZGBjQaDdatW4fXX38dVqvV5fm++oxWqxXDhw/Ha6+95vb3GRkZXr0eIYR4I1TOQ96eC5wzdbv6nXB8/8tf/iI23W+rf//+Lv8fTOdcQgghhBBPUbCLBNzYsWMB2Er8BEqlEjfffDNWr16NF198EV988QXuuuuuDi+yu6Nfv344cOBAp8/p06cP9u3bB6vV6pLddfjwYfH33hCef+zYsXa/O3LkiMv/C3fLa2trXX7eNpssKysLALr8LB1NmL766isYDAasXbvW5Y59T8oIPdm2/fr1w969e3HhhRd2u1yHEEJ8IZjPQ56eC7pDOH+o1Wq3QbzuomM6IYQQQoINlTESv9mwYYPbO7/r1q0D0L6M77bbbkNNTQ3uvvtuNDY24tZbb/XpeGbMmIG9e/fi888/b/c7YZyXXXYZSktLxVWmAMBsNuONN95AZGQkpkyZ4tV7pqamYtSoUXjnnXdcygN//PFHl/IZwBYYUyqV2Lx5s8vP//Wvf7n8f2JiIiZPnoyVK1eiuLjY7ecAIPaYaTthEiZuzs+tq6vDqlWrvPpszjzZtjfccAPOnDmDFStWtHtOS0sLmpqauv3+hBDiTiieh/r16wcALucCi8WCt956q8fvn5SUhKlTp+Lf//63S6BPUFFR0a3XjYiIaHeuIYQQQgiREmV2Eb+577770NzcjGuuuQbZ2dkwGo3YunUrPvzwQ2RmZrZrqDt69GgMGzZMbGQ+ZswYn47nr3/9Kz755BNcf/31uP3225GTk4Pq6mqsXbsWy5cvx8iRIzF37lz8+9//xuzZs7Fr1y5kZmbik08+wZYtW7B06VJERUV5/b6LFy/G5ZdfjvPOOw+33347qqur8cYbb2Do0KFobGwUn6fX63H99dfjjTfeAMdx6NevH77++mu3vaz++c9/4rzzzsOYMWMwd+5c9O3bF0VFRfjmm2+wZ88eAEBOTg4A4G9/+xv+9Kc/Qa1WY/r06bj44ouh0Wgwffp0cUK3YsUKJCUluZ38+Grb3nbbbfjoo49wzz33YMOGDZg4cSIsFgsOHz6Mjz76CN9//72YbUEIIb4QiuehoUOHYsKECVi0aBGqq6sRFxeHNWvWwGw2+2QMy5Ytw3nnnYfhw4fjrrvuQlZWFsrKypCfn4/Tp09j7969Xr9mTk4O3nzzTTz33HPo378/kpKSxMVHCCGEEEIkIcEKkIQR3377LX/77bfz2dnZfGRkJK/RaPj+/fvz9913H19WVub2b1566SUeAP/CCy+0+52wlPrLL7/c7ncA+KeeeqrLMVVVVfHz58/n09PTeY1Gw/fq1YufNWsWX1lZKT6nrKyMnzNnDp+QkMBrNBp++PDh/KpVq3o0lk8//ZQfPHgwr9Vq+SFDhvCfffYZP2vWLL5Pnz4uz6uoqOBnzJjBh4eH87Gxsfzdd9/NHzhwgAfQbgwHDhzgr7nmGj4mJobX6XT8oEGD+CeeeMLlOX//+9/59PR0XqFQuCxlv3btWn7EiBG8TqfjMzMz+RdffJFfuXJlu+Xu+/Tpw19++eXtPuOUKVPaLTPvybY1Go38iy++yA8dOpTXarV8bGwsn5OTwz/zzDN8XV1du/chhJCeCNXz0IkTJ/i8vDxeq9XyycnJ/GOPPcb/+OOPPAB+w4YN4vOmTJnCDx061KtxCq8/c+ZMPiUlhVer1Xx6ejp/xRVX8J988on4nFWrVvEA+J07d7r87YYNG9qNo7S0lL/88sv5qKgoHkC78wMhhBBCSKBxPE8dRknw+Mc//oEFCxagqKjI7SqAhBBCiD/ReYgQQgghJPRRsIsEDZ7nMXLkSMTHx/eoWTohhBDSHXQeIoQQQgiRB+rZRSTX1NSEtWvXYsOGDdi/fz++/PJLqYdECCGEIXQeIoQQQgiRF8rsIpIrKipC3759ERMTgz//+c94/vnnpR4SIYQQhtB5iBBCCCFEXijYRQghhBBCCCGEEEJkQyH1AAghhBBCCCGEEEII8RUKdhFCCCGEEEIIIYQQ2QiJBvVWqxVnz55FVFQUOI6TejiEEEJgW7muoaEBaWlpUCikvXdC5wlCCAk+wXSe6A46txBCSPDx9NwSEsGus2fPIiMjQ+phEEIIcePUqVPo1auXpGOg8wQhhASvYDhPdAedWwghJHh1dW4JiWBXVFQUANuHiY6Olng0hBBCAKC+vh4ZGRniMVpKdJ4ghJDgE0znie6gcwshhAQfT88tIRHsEtKGo6Oj6URDCCFBxpPSjjNnzuCRRx7Bt99+i+bmZvTv3x+rVq3C2LFjAdjSkZ966imsWLECtbW1mDhxIt58800MGDDAqzHQeYIQQoJPqJYA0rmFEEKCV1fnltArnieEEBJSampqMHHiRKjVanz77bc4ePAgXn31VcTGxorPeemll/DPf/4Ty5cvx/bt2xEREYFp06ahtbVVwpETQgghhBBCQlFIZHYRQggJXS+++CIyMjKwatUq8Wd9+/YV/5vneSxduhSPP/44rrrqKgDAu+++i+TkZHzxxRf405/+FPAxE0IIIYQQQkIXZXYRQgjxq7Vr12Ls2LG4/vrrkZSUhNGjR2PFihXi7wsLC1FaWoq8vDzxZ3q9HuPHj0d+fr7b1zQYDKivr3d5EEIIIYQQQghAmV2EEEL8rKCgAG+++SYWLlyIxx57DDt37sT9998PjUaDWbNmobS0FACQnJzs8nfJycni79pavHgxnnnmGb+PnRBCCCGEEMBWjWA2m2GxWKQeiqwplUqoVKoe93ukYBchhBC/slqtGDt2LF544QUAwOjRo3HgwAEsX74cs2bN6tZrLlq0CAsXLhT/X1iVhRBCCCGEEF8zGo0oKSlBc3Oz1ENhQnh4OFJTU6HRaLr9GhTsIoQQ4lepqakYMmSIy88GDx6MTz/9FACQkpICACgrK0Nqaqr4nLKyMowaNcrta2q1Wmi1Wv8MmBBCCCGEEDur1YrCwkIolUqkpaVBo9GE7CqzwY7neRiNRlRUVKCwsBADBgyAQtG97lte/9XmzZsxffp0pKWlgeM4fPHFF13+zcaNGzFmzBhotVr0798fq1ev7sZQCSGEhKKJEyfiyJEjLj87evQo+vTpA8DWrD4lJQXr168Xf19fX4/t27cjNzc3oGMlhBAS/DIzM8FxnMtjyZIlLs/Zt28fJk2aBJ1Oh4yMDLz00ksSjZYQEuqMRiOsVivS0tKg1+sRFhYGnU5HDz88wsLCoNfrkZaWBqvVCqPR2O1/N6+DXU1NTRg5ciSWLVvm0fMLCwtx+eWX4/zzz8eePXvw4IMP4s4778T333/v9WAJIYSEngULFmDbtm144YUXcPz4cXzwwQd46623MG/ePAAAx3F48MEH8dxzz2Ht2rXYv38/Zs6cibS0NFx99dXSDp4QQkhQevbZZ1FSUiI+7rvvPvF39fX1uPjii9GnTx/s2rULL7/8Mp5++mm89dZbEo6YEBLqupthRLzni23tdRnjpZdeiksvvdTj5y9fvhx9+/bFq6++CsBWuvLrr7/i9ddfx7Rp07x9e0IIISFm3Lhx+Pzzz7Fo0SI8++yz6Nu3L5YuXYpbbrlFfM7DDz+MpqYmzJ07F7W1tTjvvPPw3XffQafTSThyQgghwSoqKkosg2/r/fffh9FoxMqVK6HRaDB06FDs2bMHr732GubOndvhaxoMBhgMBvH/aaVfQggJXX4PTebn57ssJw8A06ZN63A5eYCWlJerF9YdwsOf7AXP81IPRTJf7T2Li17bhOPlDVIPRTJl9a24ZOlmvJtfJPVQSABdccUV2L9/P1pbW3Ho0CHcddddLr/nOA7PPvssSktL0draip9++gkDBw6UaLTSWCseHxqlHgohhAS9JUuWID4+HqNHj8bLL78Ms9ks/i4/Px+TJ092aWw8bdo0HDlyBDU1NR2+5uLFi6HX68UHLXwib60mC2a8uRUvrDsk9VCIhI6XN6C8vlXqYRA/8Huwq7S01O1y8vX19WhpaXH7N3SikR+e5/HW5gJ89NtpHC1jdyJ33/9+x7HyRsz/4HephyKZZRuO43BpA5788g+ph0JIULnffnx4YA27xwdCCPHE/fffjzVr1mDDhg24++678cILL+Dhhx8Wf9/R/EP4XUcWLVqEuro68XHq1Cn/fAASFL74/Qx2nazBW5sLpB4KkciZ2hbkvbYZ57ywvusnk5ATlEWndKKRt+qm7jeZk4uSOnbvHtC6JYR0rqLB0PWTCCFEZh599NF2TefbPg4fPgwAWLhwIaZOnYoRI0bgnnvuwauvvoo33njDpQSxO7RaLaKjo10eRL5O17hPvCDs2F5QJfUQ/Gr27Nni8VOj0aB///549tlnXTJhu2vjxo3gOA61tbUdPufTTz+FUqnEmTNn3P5+wIABWLhwYY/H0hGve3Z5KyUlBWVlZS4/KysrQ3R0NMLCwtz+DS0pL291LSaphyC5+lZ2t4E+TC31EAgJao2Gnl+AEEJIqHnooYcwe/bsTp+TlZXl9ufjx4+H2WxGUVERBg0a1OH8A0CHfb4Ie8qodI15lY3yv8F4ySWXYNWqVTAYDFi3bh3mzZsHtVqNRYsW+f29r7zySsTHx+Odd97BY4895vK7zZs34/jx47jjjjv89v5+z+zKzc11WU4eAH788UfmlpNnuU8VADh/fJYDPQKWd4dop2AX698LQtxpNlqkHgKR2PpDZVi796zUwyAkoBITE5Gdnd3pw7kHl7M9e/ZAoVAgKSkJgG3+sXnzZphMjmvOH3/8EYMGDUJsbGxAPg8JfmWUSc28qsbuVRzxPI9mo1mSh7fzJ61Wi5SUFPTp0wf33nsv8vLysHbtWgBATU0NZs6cidjYWISHh+PSSy/FsWPHxL89efIkpk+fjtjYWERERGDo0KFYt24dioqKcP755wMAYmNjwXGc25sVarUat912G1avXt3udytXrsT48eMxdOhQrz6PN7zO7GpsbMTx48fF/y8sLMSePXsQFxeH3r17Y9GiRThz5gzeffddAMA999yD//u//8PDDz+M22+/HT///DM++ugjfPPNN777FEHuVHUzZry5FbMnZuLPU/tLPRzJ1VNmF9OidY5gV6vJijCNUsLREEJI8Lnjnd8AAOMyY5Gqd58FTwir8vPzsX37dpx//vmIiopCfn4+FixYgFtvvVUMZN1888145plncMcdd+CRRx7BgQMH8I9//AOvv/66xKMnwYSakpOKbmZ2tZgsGPLk9z4ejWcOPjsN4ZruF+iFhYWhqspWvjl79mwcO3YMa9euRXR0NB555BFcdtllOHjwINRqNebNmwej0YjNmzcjIiICBw8eRGRkJDIyMvDpp59ixowZOHLkSKdVe3fccQdee+01bN68GZMnTwZgiyl98sknfj8me72VfvvtNzGKB0CssZw1axZWr16NkpISFBcXi7/v27cvvvnmGyxYsAD/+Mc/0KtXL/znP//BtGnTfDD80PDP9cdQ3mDAS98doWAX2A52cRzbWV0AXIJbdS0mCnYRQkgHzta2UrCLkDa0Wi3WrFmDp59+GgaDAX379sWCBQtc+r7o9Xr88MMPmDdvHnJycpCQkIAnn3wSc+fOlXDkJNhQGaPv8DyPjUcr8P62Yhw8WweFgsPIXjG4cVwGJg1IAMcFZ9delvqk8jyP9evX4/vvv8d9990nBrm2bNmCc889FwDw/vvvIyMjA1988QWuv/56FBcXY8aMGRg+fDgA11LyuLg4AEBSUhJiYmI6fN8hQ4ZgwoQJWLlypRjs+uijj8DzPP70pz/56dPaeB3smjp1aqepc+5S1KZOnYrff2d3dSmdmibzzntMfSu7/WiidWrqWeakrsWEFL1O6mEQQkhQov5thLQ3ZswYbNu2rcvnjRgxAr/88ksARkRCVU0zXZP7QqvJgkc/3Ycv9riW35+uacE3+0tw0ZBkLL52OBIig68nd2U3yxjD1EocfFaa5J0wL2MLX3/9NSIjI2EymWC1WnHzzTfj6aefxvr166FSqTB+/HjxufHx8Rg0aBAOHToEwLby7b333osffvgBeXl5mDFjBkaMGOH1mG+//XYsWLAAb7zxBqKiorBy5Upcf/31iIqK8vq1vBGUqzHKDTXkdsVyZld0mN/XhAgp1L+NEAetik7JxLWXYSPDN4cIIYQEP7PFij+/vxtf7DkLpYLDnef1xSf35GLN3AmYfW4m1EoOPx4sw9XLtqCwsknq4bZT1c0yRo7jEK5RSfLwNkvu/PPPx549e3Ds2DG0tLTgnXfeQUREhEd/e+edd6KgoAC33XYb9u/fj7Fjx+KNN97wensJGVwfffQRjh07hi1btvi1Mb2ArqwDwDnAQQ252Q5wUODTVR3dUSNEFKWjYDhx1USZXYQQ4hetJsdiMBolTYm769Ufj+Lnw+XQqhT47+3n4PErhmBsZhwmZMXj6SuH4st55yEzPhyna1pw/fJ8FFc1Sz1kF9VN3cvsCiURERHo378/evfuDZXKca05ePBgmM1mbN++XfxZVVUVjhw5giFDhog/y8jIwD333IPPPvsMDz30EFasWAEA4oIhFkvXCytFRUXh+uuvx8qVK7Fq1SoMHDgQkyZN8tVH7BB9swPAuSF3i4nNVbacg3z1LexevLs2Z2d0X3D6b5YDn4S0FaGlYBdxRWWMhBDiH869muhmU/fsLq7BvzedAAC8dsMonNs/od1zhqRF4+N7zkV2ShQqGw2YtWoHaoIowGS2spuIMmDAAFx11VW466678Ouvv2Lv3r249dZbkZ6ejquuugoA8OCDD+L7779HYWEhdu/ejQ0bNmDw4MEAgD59+oDjOHz99deoqKhAY2Njp+93xx13YOvWrVi+fDluv/12v38+gIJdAdG2ITfrWA5wOE9mWd4OAvo+EOIQ0YOVdYh8OCeAU7CLEEL8o7Kb5WvEhud5PPvVQVh54NrR6bh8RGqHz02M0uKd289BekwYCiub8JeP9wZFtZOF4UCXYNWqVcjJycEVV1yB3Nxc8DyPdevWQa22JWhYLBbMmzcPgwcPxiWXXIKBAwfiX//6FwAgPT0dzzzzDB599FEkJydj/vz5nb7Xeeedh0GDBqG+vh4zZ870+2cDutGgnvRMXYuJ+ZWVWA5wKJxKrOtbzEjyb0++oMdylh8hbUU6BcPNFitUVFbBPCpjJIQQ/+huY3Ji8/0fZdhzqhbhGiUevSy7y+cnR+vw1swcXPOvrVh/uBxv/1qIOydldfl3/lTb7NgH5FrK6m7xQGexsbF49913O/x9V/25nnjiCTzxxBMej+fw4cMeP9cX5PmvGsRY7VHkUrrGcLDL+SYGq0E/5zs5rG4DQtyJ0DqygJsMbJY5E1cNFOwihBC/cC5jJN5b8UsBAGDOxEwkRXm2svrQND2euNxWAvfid4dxrKzBb+PzRI1TsEul9K7pOwkNFOwKMJrcA01GmsQBVMYI0DYgxJlzJleDgb4brHK+OUSZXYQQ4h/OZYxUzOadvadqsetkDdRKDrPOzfTqb2+d0AcXZifBZOHx6Gf7YZWwlLC6yXGtFQRVlcQPKNgVYBTssjFbrFIPQXIsZ7gJ6PtAiHuU2UUACnYRQoi/UGZX972ztQgAMH1EmsdZXQKO4/D3q4chQqPErpM1eH9HsR9G6JnqJtoH5I6CXQFW38rmhWvbaHkDq9vB6b8p2EXbgJCONFJmFwG750pCCPE3alDfPc1GM749UAoAuC23T7deIy0mDH+dNggA8NK3h1El0b+Fc2YXkScKdgUYZbLYUPka7QsAbQNCnLmuwkeZXaxy7mvYZKRgFyGE+INLGSPVsHnsp0PlaDFZ0Cc+HKMyYrr9OrflZmJYejQaDGYs/emY7wboBeeeXbyHxay0rwSOL7Y1BbsCwPnfiTJZbCjIwW6WnzPKWiDEvUb6bhBQOSshhPgLlTF2z9o9ZwHYShg5rvtN3ZUKDn+7bAgA4IMdxZI0q69u8nxFTrVaDQBobm7213BIG8K2FrZ9d6i6fgrxJQry2NS30ESO1ZU5ndH3gRD3qFcTAeiGACGE+Etlo+eBDmLT0GrCpqPlAIArR6X1+PVy+8Xj4iHJ+OFgGV5Ydwir5pzT49f0hjfBLqVSiZiYGJSX2z5/eHh4j4J9pGM8z6O5uRnl5eWIiYmBUqns+o86QMGuAGN1ct82NZTZ7eCc5cdoKadrqZYZZovVZRU6Qtjl+HI0ULCLWc5nS+rdRgghvtditKDR6TxLhWme2XK8EiYLj74JERiYHOWT11x02WD8fLgcG45U4LeiaozNjPPJ63rCm2AXAKSkpACAGPAi/hUTEyNu8+6iYFcAOAd6qIzRhtVAjzNWA35tNbSaERuhkXoYhAQVyuwiANBqopWLCSHE16g5ffdsOloBAJgyMNFnr9k3IQLX5fTCmp2n8PpPR/H+nRN89tpdcenZ5UHEk+M4pKamIikpCSYTzeP8Sa1W9yijS0DBrgCjAIcNbQcK+AnqW00U7CKkjUYKdhE7q5WHQkGlEoQQ4isVFOzyGs/z2HjEFuyaOsh3wS4AmH9Bf3y6+zS2HK/CtoIqTMiK9+nrd6Sqm6WsSqXSJ4EY4n9UOxRgrAZ52kbLWd0OzonSrG4DKmklxL22Jb4sMlmseH/7SRRUNEo9lKBBKzISQohvCc3po3S2vA9aYK9rR8saUVLXCq1K4fNgVK/YcNw4LgMA8NqPRwO24qFzZheRJwp2BRhN7G2onJOa9AtoOxDSHqurMa7ZUYy/fX4AF7y6SeqhSKbtNT6rgU9CCPEXoYwxMVIr8UhCx5bjlQCA8Vnx0Kl9n9U07/z+0CgV2FFYjfyCKp+/flutJguajY4VjyneKU8U7AoA5wtXg9mKVhMtJU5BP1v5ntVKh1baFwhpj9WeXftO10k9hKDDauCTEEL8RShfi4+kNhqe2llUDQAY39c/DeRT9WG4YVwvAMDyTQV+eQ9nlNXFBgp2SYCymoB6Ri/enQOfPA80Mlie0jZrgXqXEWLj/NVgdTXGKJ1a6iEEHVYzu6oaDfhq71kYzdSknxDiW8IqfHH2nrGBKpsLVTzPY2dRDQBgnB9XS5w7qR8UHLD5aAX+OOvfm1/d7ddFQgsFuyRAk3vK5hHUNdN2oH2BkPZYzewS+qewrG1fQ1aDXbf8Zzvu+9/veP2no1IPhRAiM7XNrsEu0rmTVc2obDRAo1RgRC+9396nd3w4Lh+RBgD4t5+zu4TMLvG6g+KdskTBrgCg5uztNdA2AECBT4AyHQlxh9UAh3Owi+6027Baxni4tAEA8Omu0xKPhBAiN9X2m82x4RTs8sQOewnjiF56v/Trcnb35CwAwNf7zqK4qtlv7yNk98VTwFPWKNglARaDXRTws2k7dWNxO9C+QEjXKLMLLo1jWcZqSauggdFgHyHEf2qaKLPLG7/Zg11j/VjCKBiWrsekAQmw8sCKX/yX3SXsAzEU8JQ1CnZJgCb3towmumtPWU0AfR8IETgfE1nN7HK+Y8xqkKPtqZHVwKeghRb1IYT4mFDCJmR20Yykc8LiMaN7xwTk/e6d2g8A8NFvp1BlXznT12pbhOw+W6/Qti0EiDxQsCsA2mXzUJ8mmCw8XcCCAj0AbQNC3Gk1WWG2sNeYm+M48b8bqMwbALtljIQQ4i+U2eW5VpMFx8obAQDD0/3Xr8tZblY8RvTSw2C24oPtxX55D2H+QZld8kbBLgnUtbB34eouWl7LYNCvbTYbk9ugzf9TsIsQ91jNbBKwumpvW6xm+RFCiD8YzBY02cvkYynY1aVDJfWwWHnER2iQqtcF5D05jsOciZkAgP9uO+mXVXmF+Yc+jFaBljMKdkmA9abkkVpbTxYhhZhFUfZtUMtwoEfcBgwG/Ahxp20gmMVgl/MNAcrssmE12KVUcF0/iRBCvCRcdyo4IJpW4uvSgTO2EsZh6XqX7Gt/u3x4GhKjtChvMODbAyU+f/36NsEu6q4jTxTsCoC22TysZ7IIBxWWgxwxEcI2YDfgJ2wDloOehHSG9RsjLAb73GE12OW8WAEhhPiKsApfbLgGigAGb0LVfnuwK1AljAKNSoFbx/cBAKzaUuTz16fMLjZQsEsCLAa7nON9Qn08i8EuYTMIDTFrmhjcBvadISbMtg0aWs1M9iYipCsU7GIzyNMWq9uBgl2EEH8Qm9NTCaNHDpypB2DL7Aq0m8f3hkapwJ5Ttfi9uManry3MQynYJW8U7JIAy9k8ABATThk9QjPE2haWt4Hj5MJiAJglTz/9NDiOc3lkZ2eLv29tbcW8efMQHx+PyMhIzJgxA2VlZRKOWBptU+hZDXIIWC1jbLsfsNqgPkpLExBCiO8JN5pjna5DqYLNPaPZiqNlDQCAYenRAX//xCgtpo9MA+D77C5Hg3o618gZBbsCQDiAalW2zV3DYEaTMyHQw3KAQzjBspjdJlApOPHOPevfCRYMHToUJSUl4uPXX38Vf7dgwQJ89dVX+Pjjj7Fp0yacPXsW1157rYSjDQ71DB8jAQr2CVjN8It0yuwyUfYvIcRHxMyucA2oirFzhZVNMFt5RGlVSI8Jk2QMQqP6dftLUFrX6rPXbVvGSAFPeaJgVwAJpWusZ3YJgR5h2V8WOfYFNicxAuFuSh3DGW6sUKlUSElJER8JCQkAgLq6Orz99tt47bXXcMEFFyAnJwerVq3C1q1bsW3bNolHLS0WVyN0zmpiNbOrLVaDntFOwS5Ws9sIIb5X49Szi3ROyOrqnxwZ0Ob0zoal63FOZhzMVh7vbTvpk9dsNVlgsK/wSGWM8kbBrgCKccrmadu0Xu6cP62Q2cViNo/wz85yKafzvsBy7zLWHDt2DGlpacjKysItt9yC4uJiAMCuXbtgMpmQl5cnPjc7Oxu9e/dGfn5+h69nMBhQX1/v8gh1wncjTK0EQMEeVjO7+Db3l1kMegKuqzGymt1GSCjheR5bT1Ri8bpDeHDN73h67R/4Zl8JDGaL1ENzIcw/nHt2sTYv89Qxe7BrYFKUpOMQsrvW7CyG0dzzTF/hJhLHuWYRE/mhf91AsB8/hYm92cqj0WBGlI7NSHIsZfOI+4LBbEWL0YIwjVLiEQUex3FOgU929wUWjB8/HqtXr8agQYNQUlKCZ555BpMmTcKBAwdQWloKjUaDmJgYl79JTk5GaWlph6+5ePFiPPPMM34euTSiw1RoMVlQ38JmkEPAapCnrUaDGRYr7xL8YQ2rgU9CQsWp6mY89NFe7Ciqdvn56q1FSI7W4rHLBuPKkWmSZQc5E6454yLU4CD9eILZ0bJGAMCA5EhJx3HRkGQkR2tRVm/Ad3+U4kp7H6/uEkoYo3VqcUVOCnjKE2V2BVCYRgmd2rbJWS5fi2U4s0sQoVVBZZ+4MN2kPkwIfLK7L7Dg0ksvxfXXX48RI0Zg2rRpWLduHWpra/HRRx91+zUXLVqEuro68XHq1Ckfjlha0fYbISxmdjlnNbH4+TvCYhmf87yDMrsICV6HS+tx9bIt2FFUjTC1EjeM7YW/XTYYcyZmigGKB9bswcKP9qLVJH2WV7W9jDGGyhi7JJQxDkqRNrNLpVTgT+N6A4BPShmd+3VRuFPeKNgVYLGMZrI4R8sd5ZxsbQPAUabEwamck7USPqcJTCzD5Zwsi4mJwcCBA3H8+HGkpKTAaDSitrbW5TllZWVISUnp8DW0Wi2io6NdHnIhLNzA+gSf1WwedzeXaV9gc18gJNiVN7Ri5ts7UNVkxNC0aPywYDJeum4k7pqchaemD8Wmv56PBXkDoVRw+Pz3M5i5cgeajdJ+n4X5RxwFuzrVarKgqKoJADAwWdpgFwDcdE5vKBUcdhRWi0G47qKVGNlBwa4AY7lflYCas9uIQT9GM7s4AHraF5jU2NiIEydOIDU1FTk5OVCr1Vi/fr34+yNHjqC4uBi5ubkSjjLwhJsC0WFCZhfbE/wGAx0XhCbtrGe/stqkn5BgxvM8HlyzB+UNBgxMjsQHd01ARly4y3N0aiUeyBuA/95xDqK0KuworMacVTvRYpQuw6taWI0xwhHooAK29goqmmDlbeehpCit1MNBil6HvMFJAID3e5jd1XYlRoD2AbmiYFcACGUZHByZLCxmNQnEYFcLe436ncU6LVjAKtoGbPjLX/6CTZs2oaioCFu3bsU111wDpVKJm266CXq9HnfccQcWLlyIDRs2YNeuXZgzZw5yc3MxYcIEqYcuCeHii8VsHtfVGNkO9gGOwCeT+4LTf9O+QEjw+XLPWWw9UQWdWoHlt+Z0uqrduf0S8O4d5yBSq8L2wmr89ZO9ks0Bau0VFbHhGgRBC7GgdaJC6NcVFRS91gDg1gl9AACf7T6DJkP3zwvCvCM6TA2qY5Q3CnYFmGP1ObaCXc6nM709wGGx8mjowYEqFLmWczJa0uq0N7Ba1sua06dP46abbsKgQYNwww03ID4+Htu2bUNiYiIA4PXXX8cVV1yBGTNmYPLkyUhJScFnn30m8ailI5YxMt6gvqHVzOQNEedPLPRvo32B7c9PSLBpMpjx/LpDAID7LhiArMSuG5iP7h2L/8waC5WCw9f7SvB/Px/39zDbMZqt4twjLoLKGDtTWGkrYeybECHxSBwm9ktAZnw4GgxmrN17ttuv4y6zi8gTBbsCTC/2KGLvLq1Ap1YgTG1bfbCWtX5VdhznaM7OalYTxzm+D6xuA1asWbMGZ8+ehcFgwOnTp7FmzRr069dP/L1Op8OyZctQXV2NpqYmfPbZZ53265I7lhvUO7NYebQEQTNjKUWHUf82gD4/IcHmfzuKUdFgQJ/4cNw5qa/HfzchKx5/v3oYAOC1n45ie0GVv4boltA6hOMc51rAfa9E1hUFYbBLoeBwy3hbdtd72052+4aY2zJG2gdkiYJdASB8eTiOyhgFrPerAoDYCKFfFcPbIJy2ASFtOUrX2MtsavtxWc9oEktaGexZ5VrSyt7nJyRYGcwWrPilAADw56n9oFUpvfr7m87pjetzeoHngQUf7gloT0LhWBqtU0OhoPq1zhTam9NnxgdPsAsArsvpBY1KgT/O1mPPqdpuvUa9y2qMtB/IGQW7AiyW0Qb1bScw1KjfMYlhLauJd7MaYy2DEzlCOiLcbabMJjaDHM4BTrGMkfEyPipjJCR4rN1zFmX1BiRHa3H16PRuvcZTVw5Fn/hwnK1rxRNfHPDxCDtG5WueEzK7MhPCu3hmYMVGaHDFiFQAwHvbirv1GrQfsIOCXQHGap8mZxw45jPcbFl+rAf8OMSE2bZBs9ECg5ntST0hQowjXKOE0n7HmfXMJtaDPI6VOVk9T9hQsIuQ4PHhzlMAgJm5mV5ndQkitSosvXEUlAoOa/eexc+Hy3w5xA4J51ShRFzA01p8LuqaTeL8JNgyuwBHo/qv953t1lySgl3soGBXADgOnxytPmcXQ9uB+YAfYGvELWSRs7wvEOLM1kvEdiHOWpCj7XSDtc/flqOMkcVgj2NvoJ5dhASHwsom/HayBgrOVk7WE6N7x+KO82z9vp744o8era7nqbZBjiBZZDDoCCWMydFaRGhVXTw78EZnxGBIajQMZis+2XXa67933g9oH5C3bgW7li1bhszMTOh0OowfPx47duzo9PlLly7FoEGDEBYWhoyMDCxYsACtra3dGnCoYzazq8MyRra2g3MJn57REj7nXUGh4Jgt5ySkM1Fi+Rrb3wsWM3pcV2OkBvUAm/sB6VxmZiY4jnN5LFmyRPz9xo0bcdVVVyE1NRUREREYNWoU3n//fQlHLA+f7LJldU0emIjkaF2PX+/BvAFIjwnDmdoWLP3paI9fryvCsZQyejpXWNkIIDizugCA4zgxu+uD7cVe9zelzC52eB3s+vDDD7Fw4UI89dRT2L17N0aOHIlp06ahvLzc7fM/+OADPProo3jqqadw6NAhvP322/jwww/x2GOP9XjwoYgyu9o26mdzO9hKOdluzi7cSYllNPBJSFvOZRSOVfjYnuSzHuSJZrhBvTPWM/yIe88++yxKSkrEx3333Sf+buvWrRgxYgQ+/fRT7Nu3D3PmzMHMmTPx9ddfSzji0MbzPL7aWwIAmDGmZ1ldgnCNCs/ZV2d8+9dCHC6t98nrdqSu2dGg3hlja8F0qbCyGUBwrcTY1pWj0hChUaKgsgnbC6u9+lsKdrHD62DXa6+9hrvuugtz5szBkCFDsHz5coSHh2PlypVun79161ZMnDgRN998MzIzM3HxxRfjpptu6jIbTE5cG3LbJvaNBjOMZqtEI5Ke0KuJ1UAP4LwSoYm5FdecUUkrIe1FadkMcrQ9FgZyla5gxHKDeuddgcXPT7oWFRWFlJQU8RER4ZiYP/bYY/j73/+Oc889F/369cMDDzyASy65BJ999lmnr2kwGFBfX+/yIDZHyhpQXN0MrUqBCwcn+ex1z89OwiVDU2Dlgee+PuTXa+K2mV0c1bC5VWwvY+wTpJldgK3v25WjbAskfLDd80b1rSYLDPY5eHSY2mUtRpbnY3LlVbDLaDRi165dyMvLc7yAQoG8vDzk5+e7/Ztzzz0Xu3btEoNbBQUFWLduHS677LIO30euJxqOs3+phB5FLewEeto2fhQCHKw1Z3feDsI2MFt5NAagT0Gw6GhlTpYDn4S0RZldNiwGu5yPkZTZZWM0W9HK+MqkpL0lS5YgPj4eo0ePxssvvwyzufPjZV1dHeLi4jp9zuLFi6HX68VHRkaGL4cc0n78w9ZE/rz+CQjX+LaP02OXDYZGqcCvxyux4Yj7aiFfEM4p0ZTR06kztS0AgF6xYRKPpHO3jO8NAPjuQCmqmzybRwgBT44DooKwHxnxLa+CXZWVlbBYLEhOTnb5eXJyMkpLS93+zc0334xnn30W5513HtRqNfr164epU6d2WsYo5xONknoUgYNTgIPRC3iOA3RqJXRq21eQ1X0BYDfwSUhbLkEOHa3CB1CQxxH0ZHs7ALQNiKv7778fa9aswYYNG3D33XfjhRdewMMPP9zh8z/66CPs3LkTc+bM6fR1Fy1ahLq6OvFx6tQpXw89ZP14yBbsumhIchfP9F7v+HDMmZgJAHjum0MwWfxT/dJRsIvyeVydrgmNYNewdD2Gp+thtFjxqYeN6hvtNxEjNSooFJTZJ3d+X41x48aNeOGFF/Cvf/0Lu3fvxmeffYZvvvkGf//73zv8G7mdaNpmNYk9ijyMQMsRrURo4yjnZO8iXji9iNuAoUxHQroiNqhnbBW+thMOFo+NzoSbY40GM6xWtqZjbT9tHeP7AgseffTRdk3n2z4OHz4MAFi4cCGmTp2KESNG4J577sGrr76KN954AwaDod3rbtiwAXPmzMGKFSswdOjQTseg1WoRHR3t8iBAeX0r9p2uA8cBFw72fbALAOZd0B9xERoUVDTh/W0n/fIewjlVWPyDtGc0W1FWb1tILj3Ig10AcLM9u+t/OzxrVC8seBJl3wecS1mpilF+vPqmJyQkQKlUoqyszOXnZWVlSElJcfs3TzzxBG677TbceeedAIDhw4ejqakJc+fOxd/+9jcoFO3jbVqtFlqt1puhhQRxck+ZLE6la2xtg/YlfGqU1rcy1Zy9ffDXHvhsYmtfIKQjHMeJGT2sZ3axWMbofIgUgp48DzQaze2aKrOE1Uxwljz00EOYPXt2p8/Jyspy+/Px48fDbDajqKgIgwYNEn++adMmTJ8+Ha+//jpmzpzpy+EyJb+gCgAwNC0aiVH+maNF69RYcNFAPPHFAfzz5+O4fmwGInxcZta2MTnl9bRXWtcKKw9oVQokRgb/fHz6yDQ89/VBFFQ2YVtBNXL7xXf6fKF1TCQFPJngVWaXRqNBTk4O1q9fL/7MarVi/fr1yM3Ndfs3zc3N7QJaSqUSALtN4Fhchc/5n5rjODHAUddigtlPqcqhgFYiBGIiaBsQ0lYUw43JAUCoLGAy2OVEq1JAq7JdQ7FW0tlusQLGbo6xKDExEdnZ2Z0+NBqN27/ds2cPFAoFkpIcjdM3btyIyy+/HC+++CLmzp0bqI8hS1uP24Jd5/ZL8Ov73DQuA5nx4ahuMmLVlkKfv75QDk09uzp2uta2EmN6TFhINPCP1Kpw1Whbo/r/7ei6Ub1wEzGK4ZtHLPG6jHHhwoVYsWIF3nnnHRw6dAj33nsvmpqaxBr4mTNnYtGiReLzp0+fjjfffBNr1qxBYWEhfvzxRzzxxBOYPn26GPSSO3fZPABldjka9bO7HeLsgR5PmyrKifDvHxfO7jYgxJnzuULPamNy+zZgNfu3LQ7OTerZDHwKWL5WIK7y8/OxdOlS7N27FwUFBXj//fexYMEC3HrrrYiNjQVgK128/PLLcf/992PGjBkoLS1FaWkpqqurJR59aNpaUAkAXWbN9JRKqcCCiwYCAP69ucDnQe62mV0iNvMv3BL6dYVCCaPg5nM8b1QvlDFGuskapN1AfrzO37vxxhtRUVGBJ598EqWlpRg1ahS+++47sWl9cXGxSybX448/Do7j8Pjjj+PMmTNITEzE9OnT8fzzz/vuU4QIYXLPYmZXW0oFh5gwNWqaTahuMiIhBNJk/YHlYJeAtgEhrjgAMcJCJoxO8GPC1KhuMrIX7HMjWqdCRYOB+QbtrGf5EQetVos1a9bg6aefhsFgQN++fbFgwQIsXLhQfM4777yD5uZmLF68GIsXLxZ/PmXKFGzcuFGCUYeuU9XNOFXdApWCwzmZna9m6QvTR6ThXxtO4EhZA1b8UoC/TBvU9R95wOq0+rlQEh4CiUsBdyZEmtM7G5aux4heeuw7XYdPd53GXZPdlzsD7csYaReQt24Vq86fPx/z5893+7u2JxCVSoWnnnoKTz31VHfeSpZixcwudib3zpFy4aASF6FBTbMJVY1GwD+9LoOOkLkhpAULgZ4qhgI9bTMd4yPZ2waEdEXIAK5j6DzhTMhmajCYYbZYoVL6fT2doNG2r2E0o1l+wlYIUyvRYrIw+10g7Y0ZMwbbtm3r9DmrV6/G6tWrAzMgmRP6dY3MiPF5Dy13FAoOCy4aiHve24WVWwoxe2KmT26KN7SaxWtQoS8mac+xEmO4xCPxzk3n9Ma+0/vxvx3FuHNS3w5LMIXMLlqkgA3sXD0GkVgxk4WtC9e24iNsJy6WM3qEQE91I3vbgINrwK+uxeS3paYJCQXOQQ4h2MVaZpewDZxLTFjtWwbYFyvQOXpcsojV7wIhweL34hoAwLgAZHUJpg1NxvB0PZqNFizfeMInrylkx+rUCmhVrq102t5kYNkZp55doeTKkWmI0CjFRvUdETO73JUxMtpPXM4o2BUAwtdGmNzHi8Gu9ssjsySOtgOV8MFW1ivcfGEp25GQjnCcI5unrsUEq5W9iy+1khMvRFkN8ghiwtkOdunD2P78hEjt9+JaAMDo3jEBe0+O4/DQxbbeXe9uO4ny+tYev6a7fl0cFbG1czoEyxgBIMKpUf0HnTSqb9ugnkpZ5Y2CXRKIYzCjyTlSLjYmZ7B8Tbhz5FzKCQBVDAX82k7bhf5tAFvfCUI6I1yM87wj5Z4F7pr0sxbkaLeojdC/jbFm/by4WAGbn5+QYNBoMONIWQMAYHRGTEDfe8rAROT0iYXRbMWKXwp6/Hr1HTWnJyKzxYrSOltgMZQa1AuERvXfHyhFVaP7uVVnDeqJ/FCwSwJijyIGS9ecxVNWE9OlnM53UsQMN8a/E4RtzkEOrUqJcI2tzKK2hc3vhV4M8rD5+QH7YgXCojaM7gcxYY5Sd0JIYO07VQuet5W0JUXrAvreHMdh/gX9AQDvby/u8bWycAwRSsOdUfWaTVmDAWYrD7WSQ1JUYP+9fUFoVG+0WPHp7tNunyMEu6KoZxcTKNgVCPYjqDC5F4I8DQYzDGaLVKOSHIvN2dsStkFtiwkWBkuVBELQj+V9gRCBUFbBakaPDcdsZldbMeKiNmxuB9bLOAmR0u+nagEEtoTR2dSBiRiWHo1mowWrthT26LWEnl0uZYxUwubibK2thDFVHwalIjQ3zk327K7/7TjltgeX0LMrSlyN0fE52Z2JyRcFuyQQrVOLB5AaRprUuzt4sJjN0/aYK6zMyfMM9atyc+Kh3mWEtKcPdwTDWeF8dNAzvgqhwLEyJ5vbQR9OGX6ESEXo1zUqwCWMAo7jMP/8AQCA1VuKehT0FjO7qIyxQ0IJY4o+9LK6BEKj+sLKJnElUWeNYhkj7QcsoGCXBBQKTpzcV3ZQTyxnwlKwVMIHqJQKcSLD2nZwKWNksH8bIW21C3IwXsZHvZpsx0mhjI/KGNlcrIEQKR0qqQcADE/XSzaGi4ckY2ByJBoMZvw3v6jbr1PfYgtyuOvZRUcWmzL7QgDJAS5Z9SXnRvX/23Gq3e8dDeqpjJEFFOwKAHE1RqfJPfWrYrM5uzvidmAow60tWqGUkPaEYA9rmU2A7XxJZYw2rAf9hP3AygONRnYWayBEarXNRpyxl7UNTouWbBwKBYd559t6d739ayGaDN07Djh6dlGQoyNCsCslWivxSHpGaFT/3YGSdo3qG+z7T6SwHzjNz6l3m/xQsEsirJVtuTt4CI36a5rZuVvr7lOyFvjstKSVkW1ASGeEGyMsBjmczxXRjAa72vYYERvUM7QfAI7toFMroFXZLldZK+UkREoH7VldGXFhbpu6B9Llw1ORGR+OmmYTPthe3K3XEHp2OZcxhmZXKv8prbcFhkI5swtwNKo3WXiXRvVWK9+uZxeRNwp2SSQ+0hYxZ7GMURBrv4C3WHnmJjPOzRDjKKuJstsIcUO4IGepZ5fAtgohu5/fmVDO2mgww2SxSjyawOM4alJPiBQOnrUFu4akSpfVJVApFfjzVFt211u/FKDV5P0CX9Szq2tCZlegV970h5vdNKpvNlnEm2pR1LOLCRTsCgDhS+Uc4GAtm8cdjUohRtVZ7tUUx+hKhK7fB3b7txEiapP2KPZqYjSbhcoYbT0uo8PUYrYfq9tC77QyqbvVtQghvidkdg0OgmAXAFw9Oh1peh0qGgz4/PczXv99g70xubssNTqu2JSLZYyhH+yaPjINkVqVS6N6oTm9SsFBp7aFQZzbDPHUvU12KNglEdaCXR0dPFjbDu42A2vbwN31BJUxEuIgXHc5slnY+V44nytoNUYbpYITJ2csBj45cGLgt6rJgKuWbcE9/90l8agIkb9DJQ0AgiOzC7DdJL/9vL4AgBWbC2DxsgWKEOhwKV+jOkYRz/MoFRvUh3bPLsDeqH5UGgCIpa9Cc/pInUpcMI3IGwW7JCKsPlfJWNlW2+MKqyV8LisRRtBKhI7+bUZm+rcR0pWYMIYDHBxltjlz9G9j8zyht3/+o2UN2He6Dt/9UQqjmb2STkICxWSx4ni5LdgVLJldAHDTOb2hD1OjoLIJPx4s8+pvhV5NkVrq1eROfasZrSbbcTXUe3YJbrKXMn7/RymqGg2O5vS0DzCDgl0B4C41llafs2G1hM+ZEOipZizw6Xw3TejfZuWpPw9hV9sMWD3jPauojNGBxcCn86WTsC9UNzk+P+srORPiTyermmGy8AjXKNErNkzq4YgitCrcNqEPAGD5phNelR86Z/W0RbdZHf269GFq6NRKiUfjG8PS9Rhpb1T/ya7TYilrVAcLLlA1q/xQsCuQnCb3QoN6loM8gFPQj5FAj7tyTtZK+NxdmDj3b2M9AEyIuBojg5lNLgEOe7CvxWTpVjPiUOXuYlsvrMjIYODPluUnBLsc54fKBjbOmYRI4Xh5IwCgX2Jk0JV7zTo3ExqVAntO1WJHYbVHf8PzTqvwOWX1cFTHKCqTUQmjs5vERvXFYsDTdR8gckbBLonEMRbkgdik35VQzsla0M95O1AZo008rchIiAvnnl2sNc/lwCFap4JSYTtashTwc8eR2cXm8VH4LtQ4ZXZV0o0RQvzmRIUQ7IqQeCTtJUZpcV1OLwDAvzcXePQ3LSYLhC4Z7jK7CFBWbzumyqWEUSA0qi+qahZLX6NoH2AGBbsCwN0URZjYNxjMMJjZuWPdFmvN2d0RViJkrV9Vu8An7QuEcW3jWcIE32Th0cJQZpOA4zjECkEOBoM8nEupN4NljG4WK6hyyeyiYBch/iIEu/onRUo8EvfmTsoCxwE/Hy7HkdKGLp8vNKdXcECYmxI9xu4nueXI7JJXsMu5Uf3X+0oAUMCTJRTsCiDnyX20Tg2V/Y41y5N7CnAAsRG2i3iLlUd9KzsTmbaofxshAtu5IUythEZpO02zEuRoO98Q+vnVMHRccFfu7ihjZGc7OIt1kwHN2gI/hATSCacyxmCUmRCBS4elAAD+vflEl893bkzuXJYZZBWakhKCXSkyC3YBwM3jbaWMwgqezpldwVamS3yLgl0SUSg4x8UbAxdswqV72wMKayV8wp0j582gVSnF2nEWtkNHN88oy48QVxzHIZrBxuSA4xgpnCerGczscsZig3pnceHt+9dVNVJmFyH+wPM8TlQ0AQjezC4AuHtyPwDA2j1ncba2pdPnNnbRmJwApXXy7NkFAEPTbI3qBZFa2g9YQcEuCdHk3lHCx3pTcqF3GUv7QrvAJ4PbgBBn7gLBMeKKjGx+L+IYzOwSOB8hHf3b2Al2OZcVCUFPZ5UU7CLEL8rqDWg0mKFUcOgdHy71cDo0MiMGE7LiYLbyWPlrYafPbXTK7CLuldlLw5NkmNkFOLK7AOrZxRIKdgWAI5vHdXIfLzZnZ/eCzTnAwVoDZmeO5uzs7gvCNqAJjPwtWbIEHMfhwQcfFH/W2tqKefPmIT4+HpGRkZgxYwbKysqkG2SQYC6jp815QMzsamLk8wNuo55iOSeDGW4cx4lZ4M5YyIQmRApCv67eceHQqtr3twom90yxZXf9b0cx6jo5TzbYM7uoV1PHKmTas0twxYg0MdjpUsbo9ByGp6KyRcEuCYk9ilgoY+xgNcYEe7DLZOGZuGPtOIa6bomESNu+UMHQvtBWYpRtG1CwS9527tyJf//73xgxYoTLzxcsWICvvvoKH3/8MTZt2oSzZ8/i2muvlWiU0nK+L8JqX0NhG8RFsNug3pmewQb1zoRgn7MKalBPiF84VmIM3hJGwZSBichOiUKT0YL3tp/s8HkdZXZRtyYbnufFPojC3ExuIrQq/Pn8fojUqjAhK17q4ZAAoWCXhKiM0davSlhlieUghxDoYenive0FRmIke9uANY2NjbjllluwYsUKxMbGij+vq6vD22+/jddeew0XXHABcnJysGrVKmzduhXbtm2TcMSB5S67ldVgl0AIcrD4+Z2zwYUMv84yF+TG+eugUSnE3pYCalBPiH8cF5rTJ0VIPJKucRyHu6dkAQBWbSlEawcrFzfaF4DqLLOL5QqTBoMZRosVgOMGvBz9eWp/7H/6YgxMjpJ6KCRAKNgVAGJz9jY/j2eoQX1nhDsI5QwHOSiryXkbsP19kLN58+bh8ssvR15ensvPd+3aBZPJ5PLz7Oxs9O7dG/n5+W5fy2AwoL6+3uUhR6wFu9pONYTPz1Jml7vplhD0azCYYTRbAzsgiQnXTm37dlU3GWC1sjs5JcRfiqqaAQB944M/2AXYytPS9DpUNhrx+e9n3D5HyOxqGzQnNpX2OVikVgWdOrhLV3uqbVsh5/91txoyCW0U7JIQSwEO4eDhbnVXlrKahLtGbbdDAkNZTR2dRoRtUNdigsHs/s4cCV1r1qzB7t27sXjx4na/Ky0thUajQUxMjMvPk5OTUVpa6vb1Fi9eDL1eLz4yMjL8MWxJOB8eWAt2CTj7VmA5s8uZPkwNpcK2TVgK/DlrG+yy8uxuC0L86XS1LdgVzM3pnamVCtx+Xl8AwIpfCtwGwRs6KmN0NzFhkNADMV6mJYyEXRTskpAY5GEg2NWZxChbI0SWM3pYCvgJ2l5f6MPUUCttP2R5X5CjU6dO4YEHHsD7778Pnc43jU8XLVqEuro68XHq1CmfvG6wYS2zqW0ViRDgYH01RoWCQ6y9bxcr2eBt77DHhbdfKp7OFYT4lsXK43RNCwBbg/pQ8adzeiNKp0JBRRN+OtR+cZtGalDfKWGBLDmXMBI2UbArADrK5mExwOGOUMbI8nZgKcuvIwoFJ55kKxneF+Ro165dKC8vx5gxY6BSqaBSqbBp0yb885//hEqlQnJyMoxGI2pra13+rqysDCkpKW5fU6vVIjo62uUR6tyWrzFe7h4nZHYxEuwDOl7Eg9UsN+HaqW1mF8D2CsaE+ENZfSuMFitUCg6p+jCph+OxSK0Kt4zvAwB4a3NBu9931KDeGcMtu8QFsuLdHGfljnO6tcTyPiBXFOySkHOAQ+59Jzo7eLAU6OloMzg3Z5d7g8zOPh9L5ZwsufDCC7F//37s2bNHfIwdOxa33HKL+N9qtRrr168X/+bIkSMoLi5Gbm6uhCOXhnNZRTxjmV0iMcBhy+ZpNVnRYmS7vFnI8qtqYvP46G4SxnpmPCG+VmwvYUyPDRNLp0PFnImZUCs5/HayBrtO1rj8TsjsitLRaozuCDcO4imzi8gM5XJKKD7CdkAxWXjUtpjEC1k549ycVlhcha/tVhCCPAazFY0GM6J07cs1WEClvfIUFRWFYcOGufwsIiIC8fHx4s/vuOMOLFy4EHFxcYiOjsZ9992H3NxcTJgwQYohBw0hm6eqyQie52XfX6RtMDxSq4JaycFk4VHTbESYJnQyDXqq7T+10EuFlZJOYVcQ+7e5uUaiMkZCfEsIdoVSCaMgOVqHq0el4+Ndp7FicwFybssRf+fo2cXm9XVXhISDROrZRWSGMrsCqO0URaNSiD04WAr0tJVA5ZwI0yjFFWJY2Q7upuyJVMbIrNdffx1XXHEFZsyYgcmTJyMlJQWfffaZ1MMKKHdJj0KAw2i2opnBzCaO41zK905UNKK8oVXiUflXR6tBsbpYgUAoaXVGZYyE+NYpe7ArIwSDXQBw1+QsAMD3B0tRWNkk/tyTnl3yrqvonNAqgfXMLpb3AbmiYJfEWOnb1dnBQwxwMHDR2lmFIgX9gIQoe/82BvYF1m3cuBFLly4V/1+n02HZsmWorq5GU1MTPvvssw77dcmdcyA4TK2EVmU7VbMU5HC3ImVhZRMuWboZN721TZpBSSzOng1exdB+4Mx9ZhedKwjxpVDO7AKAgclROH9QInge+M8vjt5dHfXsknmytMccwS72MrtoH5A3CnZJzFG2Je871SI3B5SkKMcFvEXmvcsE7kqRHEE/NicyAFuBT0I8wXGc2KuIhWCX2yb99oyeY+WNMFl4nKhogtFsDezAJNC27J+l/QBw7AvC6dJdqwdWF24gxF9OhXiwCwDmTu4HAPhk12nxelIIdrXt2UVsKmk1RiJTFOwKALHvRCcBDpazeeIiNOA423LHzDVhduLI8mMj8On2+xClA8D294Gwq6NQfyxjQY62hCCH83GBxexPR4N6tvcDZ3RjhBDfKq5uARDawa4JWXEY0UsPg9mKd/NPAnAqY+x0NUY2bri74wh2sZfZReSNgl0SY6aMsZMTiEqpEHtxyP3CtbPTqHCCkfskrtNSzsj2k1pCWMdirybnYHiMvbel8/mhvF6+NwU6OkayltnV9oTp3LNLYy/tZTkTmhBfazaaxeNsRmzoBrs4jsNce++u/+YXoa7ZBKPFlg3cWc8uVhnMFtTbg4GsZ3axHPCUKwp2SSyJsUyWjsqiWQn6Cdw2Z48SmrOze/EubgOawBCGtU16ZCnY5e46U/j8LsEuRs4VzljN8BO+DtFhjlXUNErb5WtFg4EmJ4T4yCl7Vle0TgV9eGivWnjJ0BRkxIWhptmEVVsLxZ9HaNr07OpwZsIO4ZyiUnCIZnQ1eCJfFOwKgI5WVgIck3sWL9ydJVA5p1P/Nja2QWcBv0aDGc1Gc2AHRIjUOpi0i8EuRsu8hZ5dzucHJs6ZbQ6SQmZXTTM7/S2dKRWODSJkdhktVtS1mKQaEiGycrrG1q+rVwhndQlUSgXuPM+W3fX2L7ZgV4RG6XIcaYu9o6qN0PswLkIDRSfbh5BQRMGuAOpscs9ykAdwzuhhdztQwM/WS0FYeY7lDDdCnAnlW9UMZTw6ny8T3NwUqpBzGWMHPxcyu3geqGUg8NnZjcIWo0Usby2rZ/ecSYgvna2zHVfTYsIkHolvXD+2F2LC1WgQVmKkEka3KhhvTu+cTc9qwFPOKNglMVayeRxN+t3/npmgn31DuNsOrAT8OpvAcBzHzHeCkI60K2OMZCezy93RIcEe5HFegZGJzK421EoFou2TNZZKGd2dL1tMFiTb20CUM7KoCyH+VlJrK2NMi9FJPBLfCNeocNuEPuL/u21OT4lMYmZXPDWnJzJEwS6JCasx1jabYDBbJB6NdKgxuWuwy8pCiQrrgU9CPCRmdjEa4BAyu5yxEOxyd4iMt18zsLQvdCQp2rYtKLOLEN8otWd2pejlEewCgJm5mWLZcyT1o3KrkvHMLiJvFOwKALENi5srV32YGmql7RdVDJWotEWNyYH4CNs2MFl4pnuQiOWclNlFGNNRiFvo2VXDaIDD3QW4nLN5Omu4zvpiBc6SKLOLEJ86W2fP7NLLo4wRsM0vZozpBQBiZmxHWF3rQri2EM4vrHFepIDVfUDOKNglMYWCY6pXU0erniRGsrEqpRj3dLMZNCqF2INEzqWMXZ1IHKtSyncbENKZtsdJobSgiokAR/sDREyYul1T4XJGs3mEyQgL+4KD499e77Qio5DZxeq+QIivCZldqTLK7AKABXkDcMnQFNw+sW+733XUXoUlNfYWCbEhvgInIe5QsCsIUNmW86qUbN+hFcpa2SjR6Sjwyc42IMQTQtZnXYvJpW+VnDkfHRQKTlyJUFDZaJD9ioTuJmHxDGV2ufPXaYMAANfn9EJylFDGyPZ1AyG+wPM8SsRgl3wyuwAgKVqH5bfl4PzsJKmHEpRqmm3VJDHhbGZ2EXmjYFcAOKoYO5/cs1y2lWS/aK1hvHdZcrTtbhrLF+/CNihneBsQNnWU9agPU0Nlz2yqamLzPBHfppTRygNVMj1ndpb9GstQsMvdZrhlfG/8tHAyFl87HEnCuYJujDApMzMTHMe5PJYsWeL2ucePH0dUVBRiYmICO8gQUt1khMF+MyVZz2bvps4WUJKzWjGzi81gl8uNJTZ3AVnrVrBr2bJlyMzMhE6nw/jx47Fjx45On19bW4t58+YhNTUVWq0WAwcOxLp167o1YDliIRW/q9UYY8LVYgNJJrZDB4FPYV8olXGgp6vzSIpe/tuAkE61OTwoFJxYyljZIP8ghzsJblaJYjHIIWR2sXRzzPm6geM49E+KgkqpQHI0ZXax7tlnn0VJSYn4uO+++9o9x2Qy4aabbsKkSZMkGGHoELK6EiI10KqUEo8mcKiK0ZHZRWWMRI68DnZ9+OGHWLhwIZ566ins3r0bI0eOxLRp01BeXu72+UajERdddBGKiorwySef4MiRI1ixYgXS09N7PHi5cJRtsXvBxnEcUiirSdwGcg74CToKfApNh2mFLUIcxHL3RjaOj1ybA0QiY03qAfc3RainoYOjQb2h06b+RL6ioqKQkpIiPiIiIto95/HHH0d2djZuuOEGCUYYOuRawki6JmR2URkjkSOvg12vvfYa7rrrLsyZMwdDhgzB8uXLER4ejpUrV7p9/sqVK1FdXY0vvvgCEydORGZmJqZMmYKRI0d2+B4GgwH19fUuj1DWVVZTUjRN7gGId2lZzugRlnsWmoSySNgGVU0GmCxs9CciBOi8hEJYyITZzK4oN8EuBs+ZjqCn/D+7EMDqKPNC2BZGs5XpFYxZtmTJEsTHx2P06NF4+eWXYTabXX7/888/4+OPP8ayZcs8fk25zUE8VWpfiVFuzem9wWLMnOd51AqZXRGU2cVqKauceRXsMhqN2LVrF/Ly8hwvoFAgLy8P+fn5bv9m7dq1yM3Nxbx585CcnIxhw4bhhRdegMXScV+mxYsXQ6/Xi4+MjAxvhhlyWMhoEg4enaULJzMQ9BMPol1kNck54NfVxURcuAZqJQeeZ7NMiRB3hwdWejt2dHygMkabJMrsEunUSnEFYxb3Bdbdf//9WLNmDTZs2IC7774bL7zwAh5++GHx91VVVZg9ezZWr16N6Ohoj1+XtTmI4KxMV2LsStssYtY0GMww2xd7YbZnl9QDIH7lVbCrsrISFosFycnJLj9PTk5GaWmp278pKCjAJ598AovFgnXr1uGJJ57Aq6++iueee67D91m0aBHq6urEx6lTp7wZZsgRMllKGM7mAdgI+nVF2BdYbs6uUHBOpYzsbgdCnCUwtmpv24tPYUVKZ7IvY3RzBZ4YaTs21rea0WpidzEXQRKtyCgrjz76aLum820fhw8fBgAsXLgQU6dOxYgRI3DPPffg1VdfxRtvvAGDwXaMvOuuu3DzzTdj8uTJXo2BtTmIQKgoSI2hMkaW1DbZsrp0agV0anZ6tRF2qPz9BlarFUlJSXjrrbegVCqRk5ODM2fO4OWXX8ZTTz3l9m+0Wi20WvmsBNJVVpOQ0SSUbamVbC6SKWwHpkv4nFaXslp5KBTyvd/QeZafFmdqW1DG8L5A2NNZ1qNYxijzzK6OsFTG2Nl+EB2mgkapgNFiRWWjAb1iwwM3sAATV7LuJPMiOVqHo2WNss4IZ8lDDz2E2bNnd/qcrKwstz8fP348zGYzioqKMGjQIPz8889Yu3YtXnnlFQC2ci2r1QqVSoW33noLt99+u9vXkdscxFNna6mMkUU1jK/ESOTPq2BXQkIClEolysrKXH5eVlaGlJQUt3+TmpoKtVoNpdIRLR48eDBKS0thNBqh0dCXKz7CVrZlsvAobzAgXYZ3VTypg0/Ws1PC19Gle0KkBgoOMFt5VDYZxAwnOfGkHj6FgX2BEG8kMpLZ1dHxgcoYbTiOQ2KU7WZARYO8g12eEL4Xcs/yY0ViYiISExO79bd79uyBQqFAUlISACA/P9+lZcqXX36JF198EVu3bqVFstygBvVsqqHm9C43VFjs2yZ3XqUQaTQa5OTkYP369eLPrFYr1q9fj9zcXLd/M3HiRBw/fhxWq6PR9NGjR5GamkqBLjvnsi25ZzV1dofWsRKhvLdBZ1RKhZjBUVbH3kROIGb5MbwvEHa5O04KwR5mMrvabALn1RjjImzbQu7nig5vijAS+PREMkMrGBOH/Px8LF26FHv37kVBQQHef/99LFiwALfeeitiY2MB2G6sDxs2THykp6dDoVBg2LBh4nOIDc/zYsBYWCyKFfKtn/CM2Jw+nJrTE3nyul5u4cKFWLFiBd555x0cOnQI9957L5qamjBnzhwAwMyZM7Fo0SLx+ffeey+qq6vxwAMP4OjRo/jmm2/wwgsvYN68eb77FEGuq9UYAUcmC8t9J5xXY2R5GXFW9oXOvg80gSEs6uywlyiWMcp7NcaOtkFshOPmWJi9r0h5gwEWq/zOFV1lvybaA5+sLFbQ2WQ0iTK7mKTVarFmzRpMmTIFQ4cOxfPPP48FCxbgrbfeknpoIanRYEaryZaUkOimZJwVLE49qIyRyJ3XPbtuvPFGVFRU4Mknn0RpaSlGjRqF7777TmxaX1xcDIXCEUPLyMjA999/jwULFmDEiBFIT0/HAw88gEceecR3n0IGUmTer8qT84cQ4Gg1WVHfYoZehncZPCrnjNYBqJNtVpMn20Du3wdCvCVMQOpaTDCYLdCq2Gok69zLUqNSiOXeVY0GJEXLr9y7M6yUtHqChVWcSXtjxozBtm3bvPqb2bNnd9kPjFVCSXikVoVwjd/bOZMgUmPP7IqR4ZzLU843VBiMd8pet45o8+fPx/z5893+buPGje1+lpub6/VJiTWslG11dodWWEa8ttmEsoZWWQa7BJ033KXVpZJpZU7CMHdHB32YWuztWNVoRJoMezs64zo5W9S1mJAUpUNpfStK6lplG+zq6DyRyMhiBZ5MOmg1RkJ6TgicJzGY1dVZlQELaimzi8gcm8v+SaSzi/cUvb2Ej/FMFsrocWwDuV+8d/59YGMbEOIpjuMQHyH/jB5PAhzVTUbxGFEiw3NFV9mvrGV2edoCwirDklZCAkE4lrhb9ZbIG2V2EbmjYFeQSLGvfiL3zK6uJDGS4dYZR5YfGxMZd4TstiajBQ2tJolHQ0hgdDVVF4Iccs/o8USqGOxqkXgkgcdasKszydE6cBxgsthWMCaEeE84lrDcrwvwbLVwuaHMLtcbKiz3jJYrCnYFCbln8/CedJoFkCKU8Mnwbj3gmMx2thnEEj6ZbgNPhGtUiNLZqqzl+p0gpCMdZbKwtCJjV6UlqcINIhkfJzvaBGKwS+77gQeTDrVSIZZ1ynlfIMSfhJ5dzqvesqKzKgMWiA3qIyizi8gTBbsCwJMosXP5HstRZTHox/DKSkJZhtyz27qazDq+EzKf0BHiIRYyejo7/T01fQgA4K5JfZ0yu+R3nOwywy/S9tkrGgxMXC90Gfi09687Wyu/fYGQQBB7dkWzF+xiXU2TUMbIbmYXkTcKdgVQZxdswgnGYLairoXdsq0kCnCImV11LSa0miwSj0Y61LeLsKarwEVCpPyDXZ2ZfW4mNv5lKhZdOthxU0CGwa6uJETZJiWtJisaDWaJRyO9NIZLWgnxBSFLlMXMLmcM3Dtoh8oYXReDYXAXkD0KdgUJnVqJWHtzQDnfqe4qWZiVcs7OAp/ROhV0attXU47bwdNMhKQoNjLcCGmro7IKxyql8g92udsCHMchMyECCgXnyOyqZy/AEa5RIUKjBCDvwKfjuqHzKwehpFWO106EBALLPbtYXo3RaLaiyWi7qR5LDeqJTFGwK4gkU3N2Wa+w5SmO48Sgn5y3Q5dljLRCKSEuhIUb5Fzm7WmDYKF0rbROfqvwedLjkoWSVk+lxcj/fEmIP7Ec7GKZkNWl4IBoHQW7iDxRsCsAxOvWLhvuUmPyNPsEprLRAIOZ3RI+YTuwXJZB24AQV7R4hUNSlFZcha+qySj1cAKOVi52EDO7aulcQYi3zBYrquwrmQoZ9ayS122Trgltc6LD1FAoGE5xc8JiKavcUbAriMi5Mbkj4Nf5wTQ2XC2W8Mk5o6ersow0GTfc9fREkm7fBmdkuA0I6Q4h2FXeYJBdNlNbXd0cYn0VPrmX/ANO54ouG9RTZhch3VXdZATP27J74iLY7dvEovpWW7BLH0ZZXUS+KNgVRFKihbuT7F6wcRwnBnrOMHyXVgj0nK6hbXCmplnikRASWB0FehLt2Uxmq3yzmby5q5oq08bknsR4HA36qYwx1elGoUXmQWBCfK3cXsIYH6mFkrJ7mCJmdlEJI5ExCnYFgOPSq/OTSHosBXkAR5BDjllNnnJsAznvC100HbZvg/pWMxpa2V2hlLCjq0CPWqkQV2SUc0aPp1ju8ZjMQGaXp5KidFAqOFisPPUwI8RLwkqMSdSvy+MFlOSivsW2mm90mErikRDiPxTsCiLyDnB0vQqhIE0v3+3gaf+2NBnvC55eSkRqVWJqNYuTWULcEZvUyzzI0VWpN8D2KnwpDPTsEhYr6GpPUCo4JNsn6nLL8iPE3yrqqTk9q4QyRsrscszLPF0kh4QOCnYFkXSn8j3W7i44k3Ogx1PC6lJnaV8AQNmOoe7NN9/EiBEjEB0djejoaOTm5uLbb78Vf9/a2op58+YhPj4ekZGRmDFjBsrKyiQcsbQ6LV9jIMjhKbF8TaYBjs56XNJqta5SY9gNfBLSE0Jml9ADkTWe3ISXq/oW6tlF5I+CXQHgaTZPil4HjgMMZisqG+XZj8UTQqCH5QCHEORpMlrENGO58eQCI90p6EdCV69evbBkyRLs2rULv/32Gy644AJcddVV+OOPPwAACxYswFdffYWPP/4YmzZtwtmzZ3HttddKPOrg5Chfk3e5lifHB7mWMXpyf8OxWEGr7Bcr8IQQ+KRzBSHeEUp/Eyizizn1rUIZIwW7iHxRkW4Q0agUSIrSoqzegLO1LbJKKRYDfh48V+hdJseLVk/LMnRqJeIjNKhqMuJMbQv04fI5EXmTqEZZfvIwffp0l/9//vnn8eabb2Lbtm3o1asX3n77bXzwwQe44IILAACrVq3C4MGDsW3bNkyYMEGKIUvCk/R5MdglswBPdwhljGdlmtnVmaQo280xk4VHdbNR7OUmJ56u4gw4zhVyC3wS4m/V9sVO4mklRuYK2OrFBvUUDuBg//dnbSdgAGV2BZl0Ktuick47CvQ4bwOawMiFxWLBmjVr0NTUhNzcXOzatQsmkwl5eXnic7Kzs9G7d2/k5+d3+DoGgwH19fUuD9noZG4v9zJGb475vWIdKxjLcRW+zmI8GpUC8RFUyiiQ68qchPhbTbMt2BXHaLDLk/6QciWuxkiZXUTGKNgVAJ5m8wBAemw4ALYDHEJpSqvJippmdlfhE/t2yfTi3ZPvA/Xsko/9+/cjMjISWq0W99xzDz7//HMMGTIEpaWl0Gg0iImJcXl+cnIySktLO3y9xYsXQ6/Xi4+MjAw/f4LgkMRKg3oPDhDJ0TqoFBzMVh7lDXLaHp4F7oS+XXLdF7y515Uq05JWQvytyt42JZbRYBfLqEE9YQEFu4KMEOA4XSOvyb0399y1KqVYwim3oJ948e7JqpQyDfR4s9IJ9eySj0GDBmHPnj3Yvn077r33XsyaNQsHDx7s9ustWrQIdXV14uPUqVM+HK00PJncCzcD5Brg8IZSwSFVpudMT8g9y0/g1Y0RBvcDQnqCyhgdWCsmEXoCU4N6R7k8Y7sAEyjYFWR6yTTAIfCk9wYg30CPN9KphE/cD0rr5FmmxBKNRoP+/fsjJycHixcvxsiRI/GPf/wDKSkpMBqNqK2tdXl+WVkZUlJSOnw9rVYrru4oPOSis7IKIcBR02yCwWwJ1JACxtvJRq8YWzb06ZpmP4xGWl2dLal/m0Mve1Z8eYMBrSb5fS8I8Qeet/X8AxguY2S3itGR2RVGPbuIfFGwKwA8XY0RkHdzdm9QRo/8e3Z58n1IitJBaS9TElYMIvJgtVphMBiQk5MDtVqN9evXi787cuQIiouLkZubK+EIg5M+TA2NynbqLpf1ioyezUCEvl2nq+VznPQ04Cf3zC5v4p6x4WpEaJQA5HvOJMTXmowWGM1WAOwGu1jmaFBPmV1EviiUG2Qoo8kmTS/PQI83F+9yDXZ5k7mhVHBIidbhTG0LztS2iCVcJLQsWrQIl156KXr37o2GhgZ88MEH2LhxI77//nvo9XrccccdWLhwIeLi4hAdHY377rsPubm5TK3ECHh2fOA423eiuLoZJXWtyIgL9/u4gpmQ0cNiGWOyXgh2yTno6dmNEY7j0Cs2HEfKGnCqpgVZiZH+HxghIa7a3q9Lp1YgXENTQpZq2HieR32rrYyRGtQ7bq+xVsrKAjqyBRmhdK222YQmgxkRWnn8E4nZbR4+X+6r8Hmy+ovQv62svhUmixVqJZuJmOmxYThT24KztS3I6RMr9XBIN5SXl2PmzJkoKSmBXq/HiBEj8P333+Oiiy4CALz++utQKBSYMWMGDAYDpk2bhn/9618Sjzp4pcUIwS75BXi8vc4UM7tqZVjG2EWUJ4XKGF1kxIXhSFmDLEtaCfGHqiZboFxY2ZWwo8loEduDUGYXkTN5RFKCnKMnedcBjiidGlE6FRpazThb24IByVH+HVyQEoJdp2WW1eSNhAgtNEoFjBYrSmWYweHpcs/plO0Y8t5+++1Of6/T6bBs2TIsW7YsQCMKbl1lsrCQAexpHxUh2CWnxuSeBvyEFQjlumIvzwsrWXta0mo7R56SUUkrIf5Uw3i/LsDzm/ByI5QwapQK6NRs3kwnbKC9OwilU6BHlhMYbykUnNjDjcUSHYH4faC79UTmeA/z59NlWuIMeF9CIBwjz9S2wMrYIhbCZ29oNYuNhlkmZvnRuYIQj1TZyxhjGQ52OfNmtfBQ59yc3tPFw+SMNoF8UbArCPWSYZN64QTi6cFEyGKqbDSg2Wj217ACTrxT7eV2OFUtn4t3by8letu3QTHdrScEgPzLvL2REm1bxMJk4VEus0UsujpNhGtUiA23lZ/I6Xqhu8TMLoZvDhHijeomW7ArnoJdzKlvsffrohJGFywFPFlBwa4g5MhkYfeCTR+mht7eMJHlkoTecbZ9oVhGwS4BywE/QjrT1VdDLO2V8TnC05usKqVCLOeTS0aPN9ltaSzsCx6fK4RtIY/9gBB/q6YyRmazmursZYxR1JyeyBwFuwKhm9k8cgxweKM3bQfaBgB6xwurrTWLzTQJYZlcV2oFundXtRfD5d7U09BByOyqbDTKKiOcEH8RVmNkOdjFKqFnV7SO2ncTeaNgVxASAxxV8glwOO5Ue34HRY6BHsdiBZ7JEMsy5LMNvG3KkxKtg1ppK1Mqq6eyLSJfnn4zhJVaGwzUqwlwBDnkktkl8OQGWTr1txTpw9TixI22ByFdE8oYKdhl423PyFAmXDvoKbMLgGMhFJb2AVZQsCsI9YmPAACcrGqSeCTSovI12gYAoFRwYvaCnAKfhHSkq7IKFno1eVNZ0itWXtlN3mS3yTmzS5h0eFNk1EuON4gI8ZMqCnYxvBqjvWcXBbuIzFGwKwC8zeYRMprqW82oa2b3rr0cM7u8JZTwVTYa0WSQV1mGNxcYVNpLiCu5ljJ2564qy30u5Rzs6g6WS1oJ8VYN9exilrgaIzWoJzJHwa4gFKZRIjFKCwA4WS2P7C7xDq0XEQ5ZBrvE7eDZhojWqRFjz+CQy53q7mQI96YMN8KC7jQmpxUZmQ6GUxmjK8qGJsRz1LPLFUsVbGLPrjDq2QVAvAPP0j7ACgp2Bak+9gu2kzLq2+Ut5wCHleHG5HLs4eYtWQY+CemAR72aZJrZJeC8yP3MtJf+n6lpgcli9deQAsabHpfCflDeYIDBbPHfoCQglnN2o6SVMrsI6ZzBbEGDvWIgnuFgF6OLMYqrMVJmF5E7CnYFAO9lNg9Ak3sASI3RQangYDBbUdFokHo4kpFr1gJ9HwjpPqFJvdyCXd25rZEUpYVOrYDZystue3QlLkIDndp2KVdaR1l+slzUhRA/qLW3SVEqOAp4MEgsY6SeXUTmKNgVpIReTXLJ5hHu0HpzA0WtVIgTOrmUJIj927pRzimbbdCN2SyVphAWePPVkGvPru5QKDj0ibNldxXJ5JzpKY7jHCWtlM2EPvZrp5OVzeBpWS1COiSsxBgTpoZCwWh6UxssHTMa7Vl9wgq2rBO+ASztA6ygYFeQEi7YWM9kEe7SsrwdKKtJ3o36CekOoXztVLU8AxzelpYIxwg5rGLsbY9LsUG/zAKfjtUYPd8ZMuLCwXFAg8EsrjRHCGlPyOwS+sIStjS22q6lI7UU7CLyRsGuAPBmGXEBBThsaDvQNgDk2aifkI54MrUXjgtlDa1oNcmoV1M376pm2oNdRZXsHR96UZN6kU6tRJretj3kEPgkxF/qWuyZXeHs9usCvGupISdCZlckZXYRmaNgV5DqbS/JOFvXIoums91ZjRGQX78qIT3Wm80gljHWtMiiUX93gr8ANeon8udN+nxchAYRGiV4Hjgjs4ye7uhjb1LPYoCjV6w8y7y7e7bLTLBtj0IGA5+EeEpoUK6nnk2i0L/C9lwDZXa5EOanVMUoPxTsClIJkRqECxMZhu/WUoADSNXbGvUbzVaUN1CjfrkEPgnpCY7jZP2d8PZeu7AiY5EMgl3e9rgU+1TJcD8AvL9JJgQ+iypDf18gxF/EMkYKdjHHaLbCYLatXBylpX9/Im8U7AqA7mQ1cRwnBnrkegHrCTlNYLpLpVQgw16mUiiji3dvJzBimRLD+wJhg6ffDbktXgF0/866EPA5Vd0CiwwyYL1Bpe6u+tqvGwrpXEFIh2qFzC7q2cUc5963EVqlhCMhxP8o2BXEKKsJ6Jtou2itbDSKKdehrDurMQJA3wT7xbsMgl3dTRHumxAJQB7bgBB3vP1q0DnCIS0mDGolB6PFipI6trKhhZUoKxoMaDbKZwGP7pT9A0BmArslrYR4ypHZxXbPLmeslLAJ/brC1EqolBQKALxbCIWEFtrDg1imjAIcAm8PJpFaFZKitADYLklwBHoaJR6JdMSAXwW7+wEhznrLeNVeb5sGKxWOss6TIR788zYbXB+uFvvuyHFf8JbzYgW0jDwh7tWLPbuoZxNrhGBXBPXrIgygYFcAiNk8XgZ6suyT+wKGgzyAI8hRwHCgJ8ue4VYgo0CPt9+HfonCog2tsspeIKQ9z74bcixf60lsIlNsUi+f7eEpyvJzyIgLB8fZJnSVjUaph0NIUKql1RhFrC3IKAS7omglRsKAbgW7li1bhszMTOh0OowfPx47duzw6O/WrFkDjuNw9dVXd+dtmZOVaMvmKahgN8gDOAI9LGf0ZMkwy89bMeEaxNp7SxTRKluEuAS7KIPFqVE7g+Vrcszyc5T9ezcT1amVSNPb+lyyuC+wIjMzExzHuTyWLFni8hye5/HKK69g4MCB0Gq1SE9Px/PPPy/RiIOLUMZIPbvY00grMRKGeB3s+vDDD7Fw4UI89dRT2L17N0aOHIlp06ahvLy8078rKirCX/7yF0yaNKnbg2WNkNF0prYFrSaLxKPpme406Rdk2Uv45JDh5piPerchhN5lxdXNMFmsvh1UgPVkSi6n3mWEtOVtvCo9NgwcBzQbLahqogwWuS1o4k32ax+ZlHD6SmaCbXvQuULenn32WZSUlIiP++67z+X3DzzwAP7zn//glVdeweHDh7F27Vqcc845Eo02uNSJZYwU7BLwPbpCDR0NBgp2tSXMT+m+ofx4Hex67bXXcNddd2HOnDkYMmQIli9fjvDwcKxcubLDv7FYLLjlllvwzDPPICsrq0cDDmXeBnoSIjWI0qnA8/K5eO8OsYyR4cyu5CgdwtRKmK08TtfIo/lydwKf1LuMsMDT74ZWpURqtA6AfDJ6ejLZyGT4XCFmtclkP+gpuQU+iXtRUVFISUkRHxEREeLvDh06hDfffBNffvklrrzySvTt2xc5OTm46KKLJBxx8KgTG9RTsIuxKkZHZheVMRIGeBXsMhqN2LVrF/Ly8hwvoFAgLy8P+fn5Hf7ds88+i6SkJNxxxx0evY/BYEB9fb3LI5R1N0rMcZxTKSO7F2xCVlNhZROzpToKBecU9GM30CPH3mWE9ITQlP2UzIIc3QmGC339iqqaYA7xDFhvyXI/6EFGuCPYJaPtQdpZsmQJ4uPjMXr0aLz88sswmx39PL/66itkZWXh66+/Rt++fZGZmYk777wT1dXVnb6m3OYg7pgtVjG7h3p2safRYAt0RlFmF2GAV8GuyspKWCwWJCcnu/w8OTkZpaWlbv/m119/xdtvv40VK1Z4/D6LFy+GXq8XHxkZGd4MU1b6ySTAIdyt787dk95x4VAqOLSYLCirN/h2YAEmbofuZDUlyqOEryfxSlq0gchZd7KahL5dculj15PjQ5o+DGFqJUwWHqdCOAO2O2X/fezBndM1zbBY2bwp5IzlLD9W3H///VizZg02bNiAu+++Gy+88AIefvhh8fcFBQU4efIkPv74Y7z77rtYvXo1du3aheuuu67T12VhDlLf6ggKRlN2jwMjh07K7GpPON2yUsrKEr+uxtjQ0IDbbrsNK1asQEJCgsd/t2jRItTV1YmPU6dO+XGUwY0yWQC1UiFO6EI96NcTFOhxBPwKKhqZzfIj8udNLDxT7GPH7rFRoFBw4jnzeDlb2yMlWgeNUgGThcfZ2tAN9PlK/yTHAj8U/Asdjz76aLum820fhw8fBgAsXLgQU6dOxYgRI3DPPffg1VdfxRtvvAGDwXZT1Gq1wmAw4N1338WkSZMwdepUvP3229iwYQOOHDnS4RhYmIPUNtt6PEZpVVAp/ToVDAneLoIR6oSsvgjK7CIM8GovT0hIgFKpRFlZmcvPy8rKkJKS0u75J06cQFFREaZPny7+zGq1lRaoVCocOXIE/fr1a/d3Wq0WWq3Wm6EFtZ5kNQlljCcYDnAAtr5dhZVNKKhswrn9PQ+cyonYnF0mgc/ufB8y4yPAcba7kjXNJsRFUPo9YZtQuie3ILg3zdmd9UuMxB9n63GiohEXIbnrP5AJpYJD7/hwHC9vREFlk1jWGMrE1Ri78bcZsWHQKBUwmK04U9MirlZJgttDDz2E2bNnd/qcjnr/jh8/HmazGUVFRRg0aBBSU1OhUqkwcOBA8TmDBw8GABQXF2PQoEFuX0ducxB3altoJUaWNVGDesIQr/ZyjUaDnJwcrF+/HldffTUAW/Bq/fr1mD9/frvnZ2dnY//+/S4/e/zxx9HQ0IB//OMfskwN9rWsNpksoXr3wVGW0b3xZyVE4GfIp4SvJ4HPghDP4OhJirCwpPyZ2hYUVDQiLiLOhyMjRFrdSVYUjguFFU0hfY4Q9DQHp599e8ghs8vbf8l+iRG2YFdFI6YMTPTLmEKFSqlAVmIEDpc24HhFAwW7QkRiYiISE7u37+7ZswcKhQJJSUkAgIkTJ8JsNuPEiRPijfWjR48CAPr06eObAYcoYSXGGAp2uWAlB7TRHuyKojJGkXDtREUj8uP1Xr5w4ULMmjULY8eOxTnnnIOlS5eiqakJc+bMAQDMnDkT6enpWLx4MXQ6HYYNG+by9zExMQDQ7ufEPSGTpaHVjMpGIxKj5H23qSPO5Wus6mvvyVJWb0CjwczsHZmsxAh7sKsJYzMp2EXY1ic+HArOVpZQ0WhAUpRO6iFJSihfOxHC54ru3hCwBT7LQvqz+1K/pEhbsKu8ERdks5Plx4L8/Hxs374d559/PqKiopCfn48FCxbg1ltvRWxsLAAgLy8PY8aMwe23346lS5fCarVi3rx5uOiii1yyvVgkrMSop5UYmdTQSpldhB1eF2rfeOONeOWVV/Dkk09i1KhR2LNnD7777juxaX1xcTFKSkp8PtCQ1oMVhXRqJdJjwgCwHegR79YzvA304WokRNqCnSfkkLXQzQQU2heI3HmTnaVVKdErVuhpGNqZr866fXxIcvTsYq2vXz+Zrd4s/Pt1d1/ob98ex8roXCE3Wq0Wa9aswZQpUzB06FA8//zzWLBgAd566y3xOQqFAl999RUSEhIwefJkXH755Rg8eDDWrFkj4ciDg9CzKyaMWkEA3au2CGWNVMZIGNKtvXz+/PluyxYBYOPGjZ3+7erVq7vzlkzLSozE6ZoWFFQ2YXxWvNTD6ZaeTjkGJkcBAE5Vt6DZaEa4JjQP0D0t5xyYHInKRgOOljVgZEaM7wYWSD3cGQYk2yYwR8safDAYQoJHd2MzWYkRKK5uRkFFEyaE6DlC0NP4VGZ8hC3TrTX0M928PU8IbQ8os8tGOFfQjRH5GTNmDLZt29bl89LS0vDpp58GYEShpa7FFuygnl2uWLk/QqsxtsdawJMltARHCBiQRHcn4yI0SIi03YFieTsIQb9jMsjs6i5xGzC8HxDiLCvBsfIc63RqpdicPVT7dnV3wtXPvh8Ipe6hrqfzTqGklcUsP0I6U9tiy+yiMkY2iT27tPTv3xadKeSHgl0BIK4o1M1snkH2yT3rmSwDkmg7yCmrqdvZbfb94ExtiywmdIS05e03I0uGKzL25C6rUL52QiblfJ6ylbrbbgrJK/DZvb2hb4Ijy6+8weDjMRESuoSeXTEU7ALQ/VLpUCVkdkVolRKPhBD/o2BXCBiYYpvcHwnhAEdPe28AthI+QB5ZTd3dDHLIaurpXRN9uBpJ9oUajoXwd4IQX8mS0QIePVmtVdBPaFIvg3OFt7Jk1rerJ7QqJfrEO3q4EUJsamk1RmbxPI9GI5UxtsNYwJMlFOwKAUIZY0WDAdVNRolHI50BlOHmktXU0GqSeDTSGUj7AiEioYzxVE0LjGarxKORnpDZFaoBjp6E+/rJqG8X34PFfQT9QnxfIMQf6lqE1RipQb0zX9xsCXbNRot4bKUyxvao5F1+KNgVAGJWUzf/PkKrQq9Y24qMLE/u5ZDV1FMuWU0hfvHek5sojnLO0N4GhLjj7eQ+OVqLCI0SFiuP4upm/wwqwHqUBWzPhj5cyt75Um4rMvaU0LfrWDl7+wIhHRGCXdFhlNkDABxDaT1C+w+lgoNOTWEAIn+0l4eIUO/b5ehb1v3XEMoYQ7lXk2/KOYWgX4juCz64a0KZXaFl8eLFGDduHKKiopCUlISrr74aR44ccXlOa2sr5s2bh/j4eERGRmLGjBkoKyuTaMShheM49JVLKaMPbqoOTI4ExwGVjQZUNrLVq4lWZHRFC/wQ0p5QGRCto8we1jQIKzFqVd3unUtIKKFgV4gQ+3YxeKdaEBOuQSL1anIK9LB78U5ZfqFl06ZNmDdvHrZt24Yff/wRJpMJF198MZqaHNknCxYswFdffYWPP/4YmzZtwtmzZ3HttddKOOrA60kgWCzXoiAHwjUq9LGvyBiK58ye3BQR9oPCyiZYrKFdjiGUFPVkOjbIKcuPylMIsRECHlHUs8kFC4cIIVkgUkv/9s6E8wwDuwBzaE8PAPHg2YMrtkE0uQdgu2Nf0WDAsbJGjO4dK/VwJDFQLisy9uD7IJQxlta3oq7FRMtnB7nvvvvO5f9Xr16NpKQk7Nq1C5MnT0ZdXR3efvttfPDBB7jgggsAAKtWrcLgwYOxbds2TJgwQYphS6Y7JRViEDwEgzvu9PSOc3ZKNIqqmnGopB4T+yf4aFTBr1dsODRKBQxmK07XNIsN2lk1IDkSSgWHuhYTSutbkaoPk3pIhEjKbLGi2WgBQJldIoYSnBpbKdhF2EKZXSFCmMgcKWP77uSAJHmUr/WkP8AACnwiWqdGql4HADhOvVhCTl1dHQAgLi4OALBr1y6YTCbk5eWJz8nOzkbv3r2Rn5/v9jUMBgPq6+tdHiwTboiEep8qX53dBjGaDa1UcGKfqlDfF3xBq1KKTfsPlbB9jCAEcGR1AbQaH4saDbYSVvq3J6ygYFeIyEqMEO9OljeEXg8ScVWlHt4+EScwIRrs8sVEziWrqTn0VmT0Vax2gEwm96yxWq148MEHMXHiRAwbNgwAUFpaCo1Gg5iYGJfnJicno7S01O3rLF68GHq9XnxkZGT4e+h+15OvhnBsLKhogslCKzIOTg3d40NPe1xmyyTQ51iNsWfXDYNTowEAh0pCe3sQ4gtCsCtMrYRaSdNAZyykEjRQZpdbwnmG4XwS2aKjXAD1JNCjUyuRGR+6PUh8xXHRWs9shlu0Ti2uzvlHSZ3Eo5GOMJk9eJbu1oeSefPm4cCBA1izZk2PXmfRokWoq6sTH6dOnfLRCENTekwYIjRKGC1WFFWG/kp8Pa0qyU6xnSuOljWEfO8qb2WnyiPY5SvCvkCZXYQA9fbm9NSvi01N9p5dEVqlxCMhJDAo2BUAvrrMpgs2W6mOggMqG40hmeHmK0PTbPtCKAd6eprlNzRNDwA4yPD3IdTMnz8fX3/9NTZs2IBevXqJP09JSYHRaERtba3L88vKypCSkuL2tbRaLaKjo10ectGdRBaFghMXMgnFbCaBr25i9I4LR5haCYPZiqKq0A/+eWOQcK1QSsdGILSz/AjxNWpO3x5DLbvQbLL1a4vQ0L8/YQMFu0LIEHuA44+QDHB0f3UpZ2EapbjaVCgGehxlGT17nSGp9kBPKG4DH73OEHuW3+ES9jI3Qg3P85g/fz4+//xz/Pzzz+jbt6/L73NycqBWq7F+/XrxZ0eOHEFxcTFyc3MDPVzJ9DTOI/TtooyeNsG/ECtf62nZv1DGWFTZhFb7xCYUObZDzwjnioKKxpDeHoT4QoM9syuaFvZph4WKkWaDPdhFZYwuHPMy+e8DrKFgVwD1NMAhZPMcOMtu6Rrg2A5/MLwdhoZ04NM3+iZEIEytRIvJgsJKdpv1h4J58+bhvffewwcffICoqCiUlpaitLQULS0tAAC9Xo877rgDCxcuxIYNG7Br1y7MmTMHubm5zK3E2BOh3tPQhQ9utQ8WM93YOk4mRWkRG66GlWd7IRNBYpQWcREa2h6EwDmzi4JdLGoy2v79wzVUxkjYQMGuAPDVjQKhbKuwskmsuWZRaGe4+cbQdNs2OB7Cd6p7GvxVKjixPIXlfSEUvPnmm6irq8PUqVORmpoqPj788EPxOa+//jquuOIKzJgxA5MnT0ZKSgo+++wzCUcdeuSwAqEvb6wLGU6slf5zHCfuC6wF+tzhOMe5grV9gZC2qGdXez29Hg0llNlFWEPBrhCSGKVFSrQOPB96F2y+KkcAQrtXE++j9NiUaB1iw9WwWHkcDbEsDl9OZofIoHcZC3ied/uYPXu2+BydTodly5ahuroaTU1N+Oyzzzrs1yVXPT0+CGWMxdXNTN8QEQxNt50rDpwJteNDz8v+hR6foRz4FPhiIppNfcwIAeDI7IqmYFc7DFQxipldYWrK7HLGULyTORTsCjFUvubov3Gyqlm8Q8UajuPEoB/L+wJtA0Ic4iO1SIjUAgCOlYd2uVZPF7AAbOcKjgNK61tRXt/qg1GFjmwZLFbgS8J1wx8hF/gkxLcaxMwuKmNkUbNRyOyiYJc7LAQ8WUPBrgAQ7tb7JKtJvFPNbr+q2AgN0vQ6AMAhhoMcoZ7V5IvvgzCBOVhSz0RjUcKGnmSyDEqxLeBxOAQzXwHftoaN0KrQ376gyX7GzpnZwgIeFOwCAAzvZb92OltHC5oQpok9u6iMTeSLmyuhQsj6DqfVGAkjKNgVYkI1s0u4tOR8VBg/JEQzeny1GiMQuo36fVXKCdh6FCkVHKqbjChlLHODEHeEbMdQD+74qofK8PTQ2x6+KPsfmBwJjgMqGw0obwjNY6NwA8MXE9F+iZEI1yjRbLSgoCK0sx4J6QmxjJFWY2RSi4kyu9zx1fyUBB8KdoWYYfYL96NlDTCYQ7MxuS9Qk3pHsOtQSQOzd6p1aqWYuUHlKSTU+SI5cXiIZ//6OkFTyOjZfzo0t0d3hWtU6Gc/NobqvuBLSgWHYfZA8F7G9gVCnFGDerZRZlfn2JxNyRsFuwLAl9k8aXodYsLVMFt5ppfQFiZ0+07XSjsQCfVNiESERokWkwXHQ7A/j69uogyjfYHITE8yWYRj46HSBpgsVl8NKWSFYmaXr4wQj42h+dkdGeG+eb0RYuCz1jcvSEgIqhfKGKlnl4ilpB6xZxcFuwgjKNgVYjjO+e5krbSD8YIvV2MEgFEZMQCA4xWNYrPNUCBevPtgSygVnJi1sOdUTY9fL1B8nbkxqncMAOD3U7W+fWFCQlCf+HBE6VQwmq0ht1KrM1+dK4akRUPBAeUNBpSFSKmzr8r+Wc1q64iwPSizi7CsgTK7mCZmdlEZI2EEBbtCkBDo2VNcK+k4pJQYpUV6TBh4PnTvWvvCSGFfYDjQM6pXDABg76laWBkt5yTy4Iu9l+O4kC5l9GVPP8BWqtE/yd6knrFzhZDJtO9MHS3gAWCk/VxxsKSesh4Js8QG9RTsakfuh0me58XMrnANBbucCbeW5L4PsIiCXQHkq9U+RlMmCwBHRg/LgZ7RYrAr9CZxvvo+ZKdGQatSoL7VjKKqJp+8JiGhbHiIl6/52vD0GAC2oA9LhqTqoVRwqGgwhOQCHr6edPSJD0e0PevxCK1SSRglZHZFUxkjc4wWK8z2m8LUs4uwgoJdIUgs4StvRF1LaJTw8b6uY4QjoyeUgl2+7N8GAKMyYgEAR0rr0Ww0++ZFQ4xaqRD7doXSvkBIR3p6fBgWwpldAl/2UBEynPaGyPHBV6fLMI0SA+xZbRT4tGU9jrBfN7DYw40Qo9mKVpMtq5GCXQ6stOxqNjgWNqPMLsIKCnaFoPhILfrEhwNguym3c2YXqyUaKXodUqJ1sPLsleg4G0XlnEQGfHUYE4I7odik3h+H8jG9bTcFfi+uYa7UeQT17XIxPMQCn4T4knOP20gqY2zH12X0wabZZAt2aVQKqJUUAnAm3GCT+z7AItrTA0AIxPjyTrUwuf+d4b5dw9IcJRpn60KvRMNXRmaEZlaTP74PobYNCPGH3nGOcq1QblLvK9mpUQhTK1HfasaJitBbubYnhtszmUKxhFOYdPjjXLG7OHQWdSHEV4R+XREaJZQKVvKZiKDZ4Pj3J4QVFOwKUaPFYFdoXbD58tQaplEiOyUKQOg16/ftxbsta4HlQI8wgTlUUo9Wk6XzJxMS5Hp6fOA4zimDJfSCHIDvevoBtlJn4abArpPBf870ZabyCLF/G7sZ0M5y+tjOl0fLQqcNBCG+4mhOTyWMznq68m2oaBKb01NWH2EHBbtC1OjejgBHKFzA+muEjoye4J/A2Ph+S4RaVpM/9tdesWGIj9DAZOHxx9l6n78+IYHhu++GULoXCsGdQBCCHCG1PXww/8pOjYJGqUBtswlFVc09f8EQlxCpRaa9DQRldxHWiM3pwyjY4U4ITKd6RMzs0lJmV3tsBDxZRMGuAPLl12hwajQ0KgVqmk04yfAFrBD0+y2UJjA+NqKXrZyzpK4VZ2pbpB6OJDiOE/eFXSerJR4NIdIb04e+D87EYBdjAQ6tSin27dpZFFr7gqNRv28nITl94gAAuxm+biBsqqfMLqYJmV1hlNnVIbkHPFlEwa4A8Mf3RqNSiMvLh1Kgx9epwuP72i5a95+uC6nVCH158R6hVYmrr+0sDJ3JjK/voQj7wo7C0Pk+EOKOL44PY3rHguOAoqpmVDQYfDCqwPJ1Vcloe7l3QUUTqpuMvn3xIJeTaQ/0FdGxEXAEPn+j7UEYI2R2RVFzehes5PQI8yTq2UVYQsGuEHaOfXK/vaBK4pF0zV+R8l6xYUjV62C28iHRrN9f20EI9GwPgWCXv26aCN+HnUXVzK24Rkhb+jA1BibZehqGUrmWv8ryYyM06JcYASD4e10KW8BXE7Bx9kymnZTlBwAYm+loA2EOsdVKCekJ6tnFtiYD9ezqCCNt25hEwa5A8vE36ZwQCnD4C8dxtB0AjMsUspqCP/DpL0PTohGuUaKuxYSj5bQCHQk9vo7zjAnFPlV+JGb0MLY9hM9dUNGEqsbQyfITg34+noT0T4xElE6FFpMFh0vpXEHY0Wjv2RRJPZvckvttUjGzi/79O0RljPJDwa4A8NcXZ2yfWCg4oLi6GSV1bPZqApwyepgOdtkmMycqmlAZKpMZH89gVEqFOKnbwfC+QEKfr74aY8VyrdD7PvjjJuvYPsJNgdDbHj0RG6FB/6RIABT4BACFghMXcAjF7wYh3dUkNCinzB5XjGT1NNNqjIRBFOwKYVE6tdiraXtBcF+w8fb7Jf44nwglfLuLa2A0B3dJgr/uVMeEa5CdYitZCvagnz/vmpyTSVl+hAiE4O+BM/VoNVkkHo1n/HlTdUJWPABg76naoO7xKDZm9+GJQrghQsEum7HiggW10g6EkABqFFfjo2AHi5qoZ1eHGIl3MomCXQHkz0DPdobL1/olRiIuQgOD2Yr9Z2qlHo5khAy3HQzfqXbO8vNX7x9C/MXXe2yf+HAkRGpgtFix/0ydj1899GTEhSE9JgxmK89c0EfIagulFRkdQT/fv7a4OmcIbQ9CekrI7KIG9e7J/bqxWejZRcHODvEhUMxqsfI4WtaAdftL8N62k1ixuQD/zS/C2r1nceBMnfg9Jza0tweAP7844/vGY8UvhUGf2eVPHMfhnMw4fPdHKbYVVIvLirPmnL5xeDf/ZMjsC/4I/o7MiIFGqUB5gwFFVc3omxDhh3chJDRwHIdxmXH49kAptp2oEnv7hQQ/RDg4jsP4rDh8tvsM8k9UYdKARJ+/R7ASmrLvP2NbuZj1MpaRGTFQKjicrWvF2doWpMWEST0kQvyu0R7soMwuNgmZXeGU2RVyeJ7HL8cq8enu01h/qFzM0nSH44DBKdE4t188zs9OwoSseCgV7OauUWZXiBvXNw4cBxRUNqG8vlXq4XTMj3doAWB8lm0Sty3IV6YU7hr5YzMIWU2HSutR02T0wzv4hj+Dvzq1EqN6xwAAtp6o9Nv7EOJPvjw+nNvPVrq3JUS+D/6+sZ5rL2UM5nOFP8r+e8eFIz0mDCYLj51FoZLVJmwH358xI7QqDE61lf6zluVH2NVEZYxusRIGEDK7qIwxtGw9UYnp//crZq7cgS/3nEWjwYwIjRJjesfg4iHJuHpUGi4ZmoJxmbGIj9CA54GDJfX4z6+FuOU/2zH+hfV46ssDOFRSL/VHkQQd7UKcPkyNIanR+ONsPbaeqMLVo9OlHpIkJg1IAGDr1dRqskCnZu9AnhSlQ3ZKFA6XNmDLiUpcMSJN6iFJYlL/BOworMYvRytxy/g+Ug+HEI/5o4Ti3P62Y+Puk7VoMVoQxvhFrtC3a99pW6o/K5M+juMwsX88PvrtNLYcr8SUgexktXVkbJ84HDhTj9+KqjF9JJvnS8IWIbOHVmN0L/gL2Hqm2UQN6jsiJGMEUyVri9GCZ78+iP/tKAZgy8i7YWwGpo9Mwyh7drI75Q2t2FZQjS3HKvHDwVJUNhrwTv5JvJN/Euf0jcOs3ExcMiyFmWwvyuwKAH/2nQCA8+yBns1HK/zzBiGgX2IkUqJ1MJqtIdWTxNeEoN8vR4M/i8Nf34dJ9knc1hOVMFuCe8ECQvwtKyHCdmy0WPHbydA5NvrrEizDnuFktvL4jbGMnon2wOevx4L//BAIQs/TLSeCN8uPEF9qpNUYmdYsZvZRsDPYldW34sa38sVA160TeuPXRy7A01cORU6f2E4DVUlROlw5Mg0vXjcCO/6Wh9VzxuHy4alQKjjsKKzGvA9246LXNuHTXaeZmCdRsEsGptj7jmw+VgmrNYhC0k7EVQj9NIXhOM4R6AniC3l/rcYoOM++L/xyrCJoG236e1jD0/XQh6lR32rGPmrKTUKQL48PHMfh3P72UsbjwT+pD0Rz2Nx+QV7K6KcbZOf2s50jD5bUo6rR4NsX9wN/3yg8t18COA44Xt6I0rogbgNBiI9QGaN7vlz5Npg1GSmzKxSU1LVgxptbse90HWLD1Xj/zvF47urhiIvQeP1aaqUCUwclYdktY7DlkQtw3wX9EROuRkFlEx76eC8utAe9LEEaP/AFCnbJQE5mLMLUSlQ2GnColM16XMCR0cNyhts5mXHQqBQ4W9eKExVNUg9HEkqFrVwHCI0MN0IE/rrUmGgPclAfO5sJIdC3yx8So7TITrH1qcpn7LO7ow9XY0S6HgCw5Th9N4j8Ndl7NkVSsMutIL1H7KLVZMGBM3X4cs8Z/De/CP/edALv5hfhi9/PYNfJGtQ2d9yzt9lImV0d8VcyhrcqGw245T/bcbqmBZnx4fhi3kQxK7unUvQ6PHTxIPz6yAV45JJsxEVocLKqGQ99vBfT3/hVtudBOtoFgL+zmrQqJXL7xePnw+XYfLQSQ9P0fnmfYDfRfrf+cGkDyhtakRSlk3hEgRemUeKczDj8erwSvx6rQP+kSKmH1CF/nlgmDUjEuv2l+OVYBR7IG+C39yEkFAgXSvvP1KGu2QR9uFriEXXNnzfaJ9gXNGGtbxdg2xcOlzZgy3F2+zo6m9g/AXtP12HL8UrMyOkl9XAI8Rue58WeXSwd8+Sg1WTBl3vO4Ot9JdhWUAWTpfOoXK/YMIzvG4/cfvG4IDtJzAgSgp2U2RWcTBYr/vzebhRUNCE9Jgzv3zUB6X5YKThSq8K9U/th1rl98G7+SSzbcBwHS+pxy3+24/xBiVh02WAMTI7y+ftKpVuZXcuWLUNmZiZ0Oh3Gjx+PHTt2dPjcFStWYNKkSYiNjUVsbCzy8vI6fT7pnslB3rfL3+UIABAfqcWw9GgAwXuX1nHXyH8b4rwgL+cMxI2z8+yT+99P1aKh1RSAdyTEl3x7fEjR69AvMQI8H/yrMgbiznqv2HBkxIXBYuWDMrvLnzfIhGPjL8cqg7bUXeDYDv4jbI9fjwf/9iCuMjMzwXGcy2PJkiUuz/n+++8xYcIEREVFITExETNmzEBRUZE0A5ZYs9EiHl8ps8tVsFYxGs1WvLX5BHIXr8cjn+7HL8cqYbLwiAlX45zMOFw6LAXXjE7HZcNTkJsVj1S97Sb/6ZoWfLr7NP7y8V6Me/4n3Pb2dqzZUYxKe/l6OOML1QSrF789jB1F1YjUqvDuHef4JdDlLFyjwj1T+mHzX8/H7HMzoVJw2HCkApcs3YwnvzyAumZ5zJ+8Ptp9+OGHWLhwIZYvX47x48dj6dKlmDZtGo4cOYKkpKR2z9+4cSNuuukmnHvuudDpdHjxxRdx8cUX448//kB6OpsrB/rDZHsJ328nq5m7U+1s0oBEHDhTjw2HK3DNaDbv0k4akIAl3wJbT1QxuzJlRlw4+iZEoLCyCb8eq8Slw1OlHhIhXfLnXHvqoCScqCjEz4fLcRl9HzBlYCLe21aMjUcqcOHgZKmHEzDjs2yl7qdrWnC8vBEDZHT3tjvG9ImFVqVAeYMBx8obZXU3mwXPPvss7rrrLvH/o6Ic/36FhYW46qqrsHDhQrz//vuoq6vDggULcO2112L37t1SDFdSQr8uBQfo1NTFxr3gCXgfOFOHB9b8LrYk6RUbhpvH98bFQ1LQLzGiwz5jDa0m7C6uxbaCKmw6UoGDJfX45Vilyw1wyuxqT+qA56/HKvGfXwsBAK9cPxL9EgNXmRMbocHTVw7FrHMz8eK3h/HdH6V4N/8kvtlXgkcuzcZ1Y3pBEcIrN3p9tHvttddw1113Yc6cORgyZAiWL1+O8PBwrFy50u3z33//ffz5z3/GqFGjkJ2djf/85z+wWq1Yv359jwcfKgKR1dQ3IQIZcWEwWXj8GqRZTYGQN9gWcN14pBwmBlaYcGdIajTS9Dq0mCxB3aPH3yeWC7Nt+8KPh8r8+0aEhADh+7DhcHlQNyL1d9m/4PxB9u1xpJypjJ5wjQrn2kv+6dgI6NRKnGNflTFYs6FJx6KiopCSkiI+IiIixN/t2rULFosFzz33HPr164cxY8bgL3/5C/bs2QOTSR4ZC95odGpOz0pD9lD10c5TuPbNrThR0YSESA1eum4ENv31fPx5an/0T4rs9N8vSqfGlIGJeOSSbKx7YBI2/mUq/jptELISbd+NcI0S0WEU7OqIFJcDzUYzHv1sHwBgVm4fXDIsJfCDgC2WsPy2HHxw53j0T4pEVZMRD3+yD9ct34oDIbzgl1fBLqPRiF27diEvL8/xAgoF8vLykJ+f79FrNDc3w2QyIS4ursPnGAwG1NfXuzxI5ziOQ5797vSPB9m9gB2VEYv4CA3qW83YUVgt9XDaESZV/rzO4DgOeUOCd18I1InkIvs2+PlwORNL6xL58MfxYVzfOERpVahqMmLv6Vrfv0GIye0XL2Y4naholHo4Lvx9g0zIZFt/qNw/b+AjgThfAsBk+yrGG48E9/Yg7S1ZsgTx8fEYPXo0Xn75ZZjNZvF3OTk5UCgUWLVqFSwWC+rq6vDf//4XeXl5UKs77lso1zkINafvWDCF/pZvOoGHP90Ho9mKC7OT8NPCKbhhbAaU3cysyUyIwLzz+2P9win4ct5ErJ0/EVoVexUfwez1H4/idE0L0mPC8PAl2VIPB+f2T8C6+yfhscuyEa5RYndxLa78v19DtrTRq2BXZWUlLBYLkpNdU/6Tk5NRWlrq0Ws88sgjSEtLcwmYtbV48WLo9XrxkZGR4c0wmXXxEFskeP2hsqCb3AdiOXnAthLfhfbsrmAM9ASKEPj86VA5rEGcxeFPOX1iEROuRm2zCbtO1kg9HEIkpVYqMHmQbVL/c5AHOQIhXKMSV2XccDg4e136i5Dlt7u4BlX2Hi4sE64ZthVUoZ56PIaM+++/H2vWrMGGDRtw991344UXXsDDDz8s/r5v37744Ycf8Nhjj0Gr1SImJganT5/GRx991OnrynUO4pzZRYLTW5tPYMm3hwEA907thxUzxyImXOOT1+Y4DiMzYtA/iUq13ZEq4Flc1YzVW4sAAM9dPSxovp8alQJzJ/fDzw9NxfSRabDywLv5J3HBqxvxya7TIZURH9Ci7SVLlmDNmjX4/PPPodN1vFLeokWLUFdXJz5OnToVwFH6j7+/SOMybZP7mmYTfmN4cu+c4RZKX0ZfmpAVj0itChUNBmazOFRKBS7IpsAnCR3+Pl4JQY6fgrh8LRBl/4Lz7cG/DYxl9KTFhGFIajR4HthwhK1AnztZiZHISoyAycIH7SI/rHj00UfbNZ1v+zh82BYMWLhwIaZOnYoRI0bgnnvuwauvvoo33ngDBoMtgFtaWoq77roLs2bNws6dO7Fp0yZoNBpcd911nR5r5ToHoWBX16ScMny7vwQvrLPt23+5eCAeuSQ7pPskhapAJWcIXvnhCEwWHpMHJuL87Pa9z6WWotfhjZtGu5Q2/uXjvbjx39twpLRB6uF5xKtgV0JCApRKJcrKXC+Uy8rKkJLSeX3pK6+8giVLluCHH37AiBEjOn2uVqtFdHS0yyO0BeaLo1IqcGG2LdDzwx/BOZkJRJ+ASQMSoVMrcKa2BYeD9Ivo762gUSkwxT6RC9aJbSBO4RcJgc9D7AY+CRFMHZQEBQccLm3AmdoWqYcjOaFv147C6qBatTUQF9tCqftPQXwjwLEV/H+2EM4Vwbw9WPDQQw/h0KFDnT6ysrLc/u348eNhNpvF1RaXLVsGvV6Pl156CaNHj8bkyZPx3nvvYf369di+fXuHY5DfHMRGaFAfqaUStmBzpLQBCz7aA8DWs2n+BQOkHRAJiANn6rB271kAwCOXDJJ4NJ0TShsfvTQbYWoldhRV47J//oLnvzkoBtKDlVfBLo1Gg5ycHJfm8kKz+dzc3A7/7qWXXsLf//53fPfddxg7dmz3R0u6dPFQe7DrYGlQTe4DOZQwjRLn9bcFer7/w7Py2kAJ5L/IxfbJzPd/BFugJ3BjmTwwERqlAiermnG0LLj68hDSEX9N7eMiNMjpEwvAdhc5mAUiGJ6ZEIGsxAiYrTxzGU7CYi6bjlag2RjcF6qBIAT/NhypCLo2ECxJTExEdnZ2pw+Nxn1Z1549e6BQKMSV4Zubm6FQuE5zlEpboMdqZe/fWMzsopX42pGyYX+ryYIH1vyOVpMVkwYk4Ikrhkg2FpZJsQ+8ufEEAODKkWkYmqYP+Pt7S6NS4J4p/fDTQ1NwydAUWKw8VvxSiAtf3Yhv9pUE2VzTwesyxoULF2LFihV45513cOjQIdx7771oamrCnDlzAAAzZ87EokWLxOe/+OKLeOKJJ7By5UpkZmaitLQUpaWlaGxkb+IZiO/RZHtW0+maFvxxVh5NNbtDWMni6yD+8vnb+dlJ0CgVOF7eiCNlwZnh5m8RWhUmD7QFPr/ed1bi0bBt8+bNmD59OtLS0sBxHL744guX3/M8jyeffBKpqakICwtDXl4ejh07Js1gJRKII9Xlw1MBAN8EbbArsMfrS+3ninX7gnV7+MfwdD16xYahxWTBz4fZKuN0Z0zvWMSGq1HXwnYbiFCRn5+PpUuXYu/evSgoKMD777+PBQsW4NZbb0VsrC2gf/nll2Pnzp149tlncezYMezevRtz5sxBnz59MHr0aIk/QeA5Mrso2NURKWYLr3x/BIdLGxAfocFrN4yCShnQDkOkjUBNGYurmvHtAdt1x5/P7xeYN/WR9JgwLL8tB6vmjEOf+HCU1Rsw74PdmLlyBwqCbMEfoBvBrhtvvBGvvPIKnnzySYwaNQp79uzBd999JzatLy4uRkmJ46LxzTffhNFoxHXXXYfU1FTx8corr/juUwS5QGc1CaWMQmpkMAlU3PziocnQqGyBnkMlwRfoCcQdhGidGlPtpYxr9wThvhCgnWH6SNvkfu3es8wGPoNBU1MTRo4ciWXLlrn9/UsvvYR//vOfWL58ObZv346IiAhMmzYNra2tAR6pvF02PBUcB/xeXIvTNc1SD0dylw6zHR82HCkXJ4NSC8RhiuM4TB+ZBgD4KgivFYDA9m9TKjhcYL92+u5AcGWEk/a0Wi3WrFmDKVOmYOjQoXj++eexYMECvPXWW+JzLrjgAnzwwQf44osvMHr0aFxyySXQarX47rvvEBYWJuHopdFEPbuCzsGz9Vi5pRAA8NJ1I5AYpZV4RCRQ/vNrAaw8MGVgIrJTQrNU+vxBSfj+wcl4MG8ANCoFfjlWiUuW/oJXfziCFqNF6uGJuhU+nj9/Pk6ePAmDwYDt27dj/Pjx4u82btyI1atXi/9fVFQEnufbPZ5++umejp104MpRtgvYtXvOBs1KfIEeRbROLTYf/iqYMnoCvCGEfeGrfcET6An0MC4akowwtRInq5qx73RdYN+ciC699FI899xzuOaaa9r9jud5LF26FI8//jiuuuoqjBgxAu+++y7Onj3bLgOMBf4MhidF6zC+bxwA4JsgzmYKVDB8aFo0+sSHw2C2Bl2jen/fFJk+wnZ+2HCkglYhBHD5CFuW3zf7S2AJkmsn4t6YMWOwbds21NbWoqWlBQcPHsSiRYug1boGC/70pz9h9+7daGxsRHl5Ob788ktkZ2dLNGppNRpsk08KdrUnRREjz/N4au0BWHlbxvWF9r6BRP7qWkz46Dfbwhd3T3bfgzBU6NRKPJg3ED88OBlTBibCaLHijZ+P46LXN2F9kPSMplzJAApUPfDUQYmI0qlQWt+KHUXVAXnPYHTlyHQAtrvWwRLoCbQLs5MRrlHiVHULfj9VK/VwJBGuUYlLywdjtiMBCgsLUVpairy8PPFner0e48ePR35+vtu/MRgMqK+vd3mEvAAdpq6wBzmCsZQx0IdqjuPE7K5v97OV0TM4NQr9EiNgNFvxY5AuagMEbiJ6Xv9E6MPUqGgwYHtBVYDelZDAEDK7onQU7OpIIM8/X+0rwc6iGoSplfjb5YMD98akU4HYBdbuOYNWkxWDkqOQ2y8+AO/of5kJEVg9ZxyW3zoGqXodTte04I53fsOd7/yGU9XSVhFQsCsAAn3xrlUpcZn94v3LICxfC5QLspMQoVHidE0LdhfXSj0cSYRplLjI3ng32EpVAtkM8kp7uc7X+4In25E4lJbaggxCObwgOTlZ/F1bixcvhl6vFx8ZGRl+H6dcXDosBQoO2He6DkWVTVIPR3KXDbdl9Px8uDwomrUH6gjlXMoYjDcCAn2TSqNSiPtCMG4PQnqi0Sg0qKfVGKVmtlix9MejAIB7p/ZDWgx7ZbUsW7PTltX1p3MyJF0cwdc4jsMlw1Lx08IpuHtKFlQKDj8dKsNFr2/Csg3HYTBLU9pIwS6ZuspevrZuf4lkO5cz4aI1kN9p50DPZ7tPB+6NOyFcugfy0CYEetbuOQujWfoViKRIspsyKBHROhXK6g3YeoLu2MvBokWLUFdXJz5OnTol9ZB8xt/Hh/hILSYNsJV5f7IrOI6NbQXyAnB4uh6948LRYrLghyDKcArEFhDOD78cq0BZPfXHE4J/3x4oDYrzJSG+Qj27OhboeMPavWdRUNmEmHA15kzMDOybE0kdOFOHP87WQ6NS4JrR6VIPxy8itCosunQw1j0wCeP7xqHVZMXL3x/Bpf/4BVuOVwZ8PBTskqnxWfFIidahrsWEnw4GVx+SQLp+rC3bY+2es0HVLC+QJg9MRGKUFlVNRvx8OHgmcoGkVSlx1SjbSeXD3+QTFJGLlBRbNkVZmev+WVZWJv6uLa1Wi+joaJdHqAtkHPgG+7Hxk12ng6o/kRTBcI7jcO0Y2/Hh411sHR+yEiMxLjMWVj74Ap/izaEAzkTH941HUpQWdS0mbD5aEbD3JcTfaDXGrvEBOAtbrDze+Pk4AGDu5CxE6dR+f0/SNeE04++M4g/tWV2XDE1BTLjGr+8ltYHJUVgzdwKW3jgKCZFaFFQ04Zb/bMf8D3ajpK4lYOOgYFcABOLg2ZZSweH6sb0AAP/bURzw9w8WuVnxyIgLQ4PBjHVB2J8mENRKBa7Lse0LwkGWRTeOs03uvz9Qipomo8SjIc769u2LlJQUrF+/XvxZfX09tm/fjtzcXAlHJl95Q5IQG65GaX0rNh+jSf2MMbZj5NYTVZKvUhno8j0h8PnRb6eYL/NWKhylnawFPom8UYP64PDToTIU2rO6ZuVmSj0cEkBmi1XslSrMy+SO4zhcPTod6x+aglm5faDggK/3leCCVzbhn+uPodXk/0QUCnbJ2A1jM8BxwK/HK3GyStq+LI47tIF9X4WCw432C/lgCPRIUc4JOCYzm45W4Gxt4KLp7kgR/AWAYel6DEuPhtFixee/n5FkDCxrbGzEnj17sGfPHgC2pvR79uxBcXExOI7Dgw8+iOeeew5r167F/v37MXPmTKSlpeHqq6+WdNxSCMTxQatS4mp7Cv3HlO2IjLhw5GbFg+eBz3cHx/EhUOeJy0ekIlKrwsmqZmwvZHdRG4FwY2T9oXKUN1BpJ5EHKmPsTOAuyldvKQIA3HxOb/q3YMy2gmpUNxkRG67GuTJpTO8pfZgaz1w1DGvnn4exfWLRYrLgtR+P4uXvj/j9vSnYJWMZceGYbO/LsiYIAj1SuS4nAwoO2FFUjRMVjVIPRxJ9EyIwISsuKEtVAunGcb0B2AKfrK7QKZXffvsNo0ePxujRowEACxcuxOjRo/Hkk08CAB5++GHcd999mDt3LsaNG4fGxkZ899130Ol0Ug5b1q7PsU3qfzxYhspGg8SjsZEqGA447rR+svs0U8eHcI1KzGZaszOIMsHt/wSBbt87MDkKY3rHwGzlmT5fEnmhMkbpHS6tR35BFZQKDrdO6CP1cIiTQNxcErK6LhmWApWSzRDMsHQ9Pr4nF/+8aTQGJEXi7slZfn9PNrd0gAnXzFIsuHDTObbJzMe/nWa22WqKXofzByUBAN7bdlLi0UjnT/ZAz/92FMNkkX5fkOL7cOXINOjUChwpa8DOoprAD4BhU6dOBc/z7R6rV68GYEt1fvbZZ1FaWorW1lb89NNPGDhwoLSDDrBAB1iGpEVjZEYMTBYe/9seREEOiVw6PEXMcNpyXLqFLKQIswnXCt/uL6VsJgB/Osd2vlyzg0o7iTw0ipldtBpjR/x9Cn5nq20OcsnQFFqBMUj5axcwW6z47oAt2HX58DQ/vUto4DgOV45Mww8LJiMp2v83tCnYJXMXDk5GUpQWlY0GfLNfwqW0xTu00iyxOvPcTADARztPob7VJMkYAOfVGAO/HS4dnoKESA1K6lrx7YHSgL+/QMqECX2YGteMtmVvrPy1ULqBENKJQB4f5tiPje9uOxlUN0SkCIaHa1SYYW9Uv3prUeAH0EYgt8GIXjEY0zsGRosV7+Wze1NIcMWIVERpVSiubqYVfEnIM1usMNiP75TZ1V4gjrUtRgu+2mubh92WS1ldrNlWUI2aZhPiIjSYkBUn9XCCQqAWn6Fgl8yplQrMsk9mVmwuZKo0w9nkAQnonxSJJqMFHzFa0qlVKcW0aZYDPbfbl3n+4WApTlVL24iaEKldNjwVSVFaVDQYgmIRD6lPUcKNkfWHy5g7Ptxxnq2c4L3txQFpGtsVqXp9ArbAp9DTbvVWds+XRB6aDI7vM/WJksYPB0vRaDAjIy4M52RSsCPY+Psm48+HywEAF2YnMVvCKBXa2gEgZTYPYGuCqFMrcLDEViv+/+3deVhUZfsH8O+ZnW3YGRZZFUUUFUUQ9zW3Fm1508y0xdKsNPtV5tZqWrZovpZZb7a5pGmWWpringiKiAIKKCKoLCICw77M8/tjmElKC2Rmzpk59+e6ut7eYZh55mk49zn3uZ/7ESOO4/BEv2AA+jv2jSJdljApJhAKqQSn8kqRdEmcy/hCNU4YEOoBHQPWNjUKJUQI+DgqKWQSTDYkwf8Q7w0Rg/aejhgQ6gHGgG/jc/gZBE//CUZ20cDPxQ4llXX4+ZQwmvTz6fF+QeA4YO/ZItH2+yS2oapev4RRIZVAThfat2XO8LelaeOT8ZHtIJHwcz1I/p25vgMHMvTJrqFhXuZ5A3JbdMQTAVcHhbER8ZeH+b1DyccdWoP7e/rB1V6OyzeqsYunZXx89m8DAE8nJe7roV8r/uXhbF7GwHfyFwCe7K9PfG46kYeyav6WtRIiBI/EBEAhk+D05TLEC2TJFp/Hh8ebqj9/OJ5n7HPDB0vPgUwqwZS++sTnmkPZor0pZBDi6YhhYRoAwP9EXA1NrJ+hsstOQf26+FBYXoMjWdcAwLhUnohHTnElsosrIZNw6BfqwfdwRIeSXSLxZP9gcJy+jDL9arnF35/PHbYMVHIpJscGAQBW7ssSbdPZpwbol6rsSitAZqGW59HwY2CoJzpqHFFR24C1f9BFDBEWSyfD3R2VmNBbf0NkRVyWZd/8L4RwVB7c0QshHg4or2nAOpFtajIxOgDOdnJcuFZp3DmKL4YqQz4Tn9MG6G+MbEm6jOsC2bGUkNaqrtMnu+wp2XVL5j7C7DidDx0DegW6ItDdwczvRu6EOc+7DFVdvYPcoFbJzfdG5JYo2WVBfFY1BXk4YGyEDwBgRVwmfwPh2RP9guColOFcgRa/pxfyPRxedPJ2wuiu3mAM+ITnC1u+SCQcXhgWCkDfv4zPTQsIMeBzBeGMwe2hkEqQcLEEx0S63N1AIuEwY3B7AMAXh7MF0b/KUpxUcmPl6ydxWaKv7ooOdkO3ds6obdDhK7oxQqxUVZ2+QpUqu/6ZuW7M707TryYxXIcRITP9d2B/hr6qb0iYp8lfm/w7SnZZgFBaoMwaFgqOA3anFSL1ShkvY+B7lbqLvQJTmxoQfxKXJdr+NIZEz84z+cjiqbqLz+QvAIzp6oNQL0eU1zTga+rdRUTOx9kOD0XpdyrlMwnO91Jvg3GRfmjnaofiijpsSMy16HsbLrj4moOp/YKgVslwvqhCEJsW8Pld4DgOM4d0AKDv8UjVXcQaVdVTZRdfrmlrcTynBAAwsqs3z6MhllZT32i8gTi4E/Xr4gMlu0QkVOOEe7vr+zUt32vZ6i4h5ZSe7B8MB4UU6fnlxrstliKE5ZwA0NlHjZFdNGAMWL7Xshe2QvkuSCQcnm9K+n15OBulVXU8j4gQfj07pAPkUg5HL1zHkaxivofDK7lUYqzuWn3wgqiqu9QquXG5+8d7MlHfqONlHAIJFbgrXIOufmpU1TVizSF+el0S0hZVtYZkF+3EeCvmTKjvPVsIxoAIP2f4udiZ741Im5jrK3AqrxS1DTp4OikR6uVopnch/4SSXRbEd1UToK/okTTtLpR4sYTv4fDC1UGBJ5qWaSz97RzqGvg5kefb7OEdwXH66i6x7sw4NsIHnTROKK9pwMp95/keDhE5vpPhfi52mBSjb1C++Nezol/C9mCvdvBzsUNhea3olrA93i8IHo4KZBdXiq5v2V9xHIc5IzoCAL6Jz0GRtobnERHSOoZljFTZ9c/McTPWcFN9FFV1WQVTfwcMVV19QtzB8V2yLlKU7LIAvi9gbtbe0xETogMAAG/tSLN8k3aB/KE/M6g9PByVyLlehe95OJEXwjR09lHjoV76ZUvv7Ey3+JJOAUwBpBIO88Z2BgB8G5+DnOJKnkdECL9eGBYKJ5UMZ/PLseXkZYu/v3EJn8Xf+e+UMileukuf5Phs/wWLLWEzLuW0yLvdmpNKjjkjOgEAlsdloaxK3H0Nh3TyQmSAC2rqdfh4j3j7nhLrVE3LGHlRWduAo+f1yY6RXTQ8j4bw4c9klxvPIxEvSnaJ0JwRHeGklCH1iuUuZoSydM3AUSkzXsSsiMuy2BI2oc3DS3d1gp1ciuTcUuw4bZneLEJK/gLAoI6eGNjRE/WNDEt/O8f3cAjhNRnu5qDA80P1PYo+2J0Brcg3bxjXww9dfNXQ1oqv+vM/Ue3QSeOE0qp6fGzh1geAsOIlx3GYP0Z/Y2Tj8Tze+p4ScieqmnZjtJPTMsZbMdeOr8eyr6OuUQd/Nzu096QlbGJTU9+Ik7mlAPSVXYQflOyyICFU8wCAh6MSzzVdzLy/OwNl1eK8mPlPlD86aZxQVl2P93aJM8mhUavwzCB9b5bFO8+K9sJ2/pjOkHDArrQC4xbBhFiaUC7uH4sNQqC7PYq0tfjwd3FXsUgkHOY1JTm+O3YJZ/PLeR6R5cikEiy4+8/K19OXS/kdEM+igtxwb3dfMAa8td3y1dCE3KmqWv0yRgclVXZZ0qFM/S58A0M9aQmbwJnjv8+pvFLUNfXrCvFwMPnrk5ahZJclCPB8aGq/IIR4OOCattai1SxCOtRLJRzeHtcVALAhMc+iPcyEFPSmD2qPQHd7FJTX4IPdGRZ7XwFNATp5O2FqX30ftwXbUo39LQgRI5Vcineajo3fxOfgVF6p5d5cILsx3qxfBw+M6uKNRh3DvJ/OmH35v5ByKANCPXFvd1/oGDB3yxmLNqvne1fKW5k7OgwquQSJOSX4KfkK38MhpEWMlV20jNGiDjVt9DKwoyfPIyEtZcrwa1jCGEv9unhFyS6RUsqkWHJ/BABgQ2Ku8Q/SXAR07t5MdLAbJkb7AwBe23oatQ3m3XFLiPOgkkuxeJz+u/DtsUs4mWvmZvVCnAQAL93VEX4udrh8o9riO1QScjMhnBQNCPXE+Eg/MAbM3WL+Y6PQvXFvFzgqZUjOLcX6xFzLvKkAvgcAsOiecDjbyZGeX44vDot7N0JfFzs8P1S/i++b29OpWT2xClWGnl20jPGWzHGozSupwsXiSsgkHPq2pyVsYmTY/Kt3kCvPIxE3SnZZkLnWhN+pmBB3PBKjb1b/6pbTqKgVZzXL3FGd4eGoxIVrlRatbBKS/qEeuL+n/sL2/zaliLKyyUEpw9vjugAAvjycLdrdSgkxWDC2M1zt5ThXoBX9ckZvZxVeHqlv2L70t3PIK6nieUSW4+GoxIKmjTw+3pOJM5fF3a/q6YEh6OKrRll1PRZuS6XljETwquuoQb2lHWxawtgzwBVOKjnPoyH/xtRX6DodM1bFRwZQsotPlOyyACGfBs0dHQZfZxUuXa/Com2pfA+HF872cmOV2xeHLxrX2JuTsNKeeovuDoe3WoXs4kq8tT3d7O8ntOQvAAwN0+CBnu2gY8Dsjcmi34GMWJbQYoW7oxLvP9gdALDmUDYOZ5n/2GiYAyEeHx7tE4ioQFdU1DbgxR9OocFMS/qE9j0AgAd7tcOoLt6ob2R4fsNJi9wcM+5KKZAKNwO5VIL3H+wGmYTD7rRCbD1JyxmJsBluYNpTz65/ZMq89dEL+iWMA0I9TPeixOxM9R3ILq6AtqYBKrkEYd5OpnlRckco2SVyapUcKyZGQsIBW5OvYKuZdmc03PkU2Dmr0YhwDSb3CQQAzNmUgmJzbTEvxKuYJi72Cnz0cHdwnH63qd/OmGd3RgFPAQDgzfu6INDdHlfLajBv2xm6a09EbUS4Bo/20VcAz9mUgmtaMx0brYBUwuHjh3vAUSnDiUs38OmBC2Z9PyGFS47jsPSBCPg4q5DTdHNMzMfGLr7OeGGYfjnjgm2pOF+k5XlEhNxeFVV2WRRjzLg6oA8tYRQlwy6M3dq5QCaldAufaPYtSKiJnt5Bbpg9vCMA/UlbZqE4T9rmj+2MUC9HFFfU4vn1yRZtxCsUfdt7YMag9gCAV348LcoTeEelDCsmREIm4bDzdD7W/pHD95CIyAgtVMwfE45QL0dc09ZixvdJFunfJdR46e9mj7fu0y93Xr43U1S7t7rYK7D84R7Gm2NfifzYOHNIB/Tr4I7q+kY8u+6kcakYIUJjbFBPPbtuydTh5mJxJYor6qCQSdCtnbOJX52YhYm/BMlNya5IfxfTvjBpNUp2WYA13P2cOaQDYkPcUVXXiCe/OY7r5qpsEjCVXIpVk3rCQSFFfPZ1iyzlE6IXR3RE7yBXaGsb8NQ3J1BaVWeW9xHqxSwA9PB3wdzRYQCAd3amW2RpKyFCLXu0U0ixenIvOKn0FU0LfjJfVY81xMvxkX54OMofOgY8vyEZF4srTfr6Qp6DmBB3zBuj79+1eGe6WZN9fy5pFSaphMPyhyPh6aREZmEFXv4xxew7dRJyJ6iyq2WYiYKwoaor0t8FShnNuTUxVfxNbtrsKzLAxSSvR+4cJbsIAP1J26pJPRHgZo+8kmo8851p794L/aTVoKPGCcsnRILjgO+OXcJ38TkmfX0hbqX+V3KpBJ892gt+LnbIuV6FmetPoq7BdFVuQr6Qu9mT/YPxYC99/66Z60/ifFEF30MihDftPR2x6pGekHDA5qTL+OygeZfwCRnHcXhrXBf0DHCBtqYB0749YZb+fkKNE0/2D8Z/ovTHxufXJyP9ajnfQ+KNp5MSKyfqK4F3nM7HB7+Lc5MbImzVhp5dlOyyCEOyKzrYjeeRED5U1jYYV0lRc3r+UbKLGLk5KPDV1Cjj3ftZG8zXgFfIRoRrjLtuLfolDdtTrvI8IsvzcFTiyylRsFdI8cf563hx0yk0iuyONcdxWDy+K3oFukJb04DJ/0sQ1Q5shD9CTXIM7OiJhXeHAwDe35Vh8psB1kQpk2L1o72gUStxvqgCj3+dKJpdbDmOwzvjIhAd7AZtrf7YKMYl7wZ9Qtyx9IFuAIBPD1zA+oRcnkdESHOVxsouWsZ4K6beBCMxh5Jd1saU34AzV8qgY4CPswoatcqEr0zuBCW7LMCaUgQdvJzw2aReUEgl2JVWgJc2p4guyQEAMwa1xyMxAWAMePGHU9ibXsj3kCyus48an07qCblU37tq7pbToluioZRJsWZyL7T3dEB+WQ0e/V8Cispr+B4WsVGmWkJhTo/3C8ZzQzoAABb+nIbNJ/JM+vrCn4E/ealV+OaJaDjbyXEyt9RkFdHWMAcKmQRfTolCVz81rlfWYdKXCcgx8XJOw0QINfl7swd7tcOspob187edMfnfBSFtUU3LGFvEFAsPrpZW4/KNakglHHpSVY/VMUX8TWuqdu7qR/3ahICSXeRv+od6YNWknpBJOPx86ipe3pzS5mbtQt1C/HY4jsM793XF+Eg/NOgYnl13ErtS2747oXEeBL+gU29wJy+snBgJqYTD5qTLeHXL6TZX+1nDhdzN3B2VWPdUH/i72eHS9SpMWHMMl29QhRcRr5fu6oipfYMAAC//eBpr/7jI74B4FOatxtrHe8NeIcXhrGI89c0Jk1V4CT1KqFVyfPtEDDpqHFFYXosHV8cj7WoZ38PizezhoZjcJxCMAa9sOU0JLyIIjDHjMYmSXeaXdEnfq6mLrxoOSqqkEyPD0v5wHzXPIyEAJbvIbYwI1+CTpiTH1uQrmPat6U7grYVEwmHZg90wJsIbdY06PLvuJDYkim95wqiuPvjwoe7GhNf070+ipl5cu055O6uw/qk+8HOxQ3ZxJR747Khody0l5if0ZDjHcVh0d7gx4fXm9nR8sDvDpP34rOXGCAD0DHDFl49FGRNej36ZYLaNPYTGzUGBdU/1QWcfNYorajHh82M4eqHYpO8h9L8HA47j8NZ9XYwJr5d/PI3PD16wmj6VxDbVNuhgKMq3o2SX2Z2+XApAv9ERsR6mPOcw3PTp4kvJLiGgZJcFWdPJOwCMifDBF4/1gkouwYGMa5iw5hiullbzPSyLkkklWDmxJyZGB0DHgNe2nsHS386JbmnnuEg/rH60F5QyCfaeLcTEL46hUGTL+fzd7LFlRt8/qxg+O4r9ZtyJjIiPNV0TSyQcXr8nHC+N6AgA+O/+85j+fRK0NW1r1G5Nc3Czvh088P1TMcYljfd/evSON7WwtjnwdFLih2f6GHt4Pfa/RKz942KbkzzWsKz3rwwJryf6BQMAlvx2Dgu2pYqy/ykRBsNOjAD17Po3pjjipOTpEx3d27mY4NWIpbU1/tY2NBpjfxdaxigIlOyyAGs7cb3Z0DAN1k/rA1d7OU5fLsPdK4/gSNad37W1rnSfnlTC4d3xXfH8UH2fmtUHL2Dq2kTcqLzzO/dWlvcEoK/2+/aJaKhVMiTnlmLsJ0dwvKkJ552wtuQvoK/w2vRMLKICXVFe04Anvj6O/+7LEl0vM0IA/d/w88NC8f4D3aCQSrA7rRDjVv2BLJFWPfYMcMWmZ2Lh46xCdnElxq36A3va0O/Rmo6R+iWN0bivhy8adAxvbk/H7B9OobJWXBXhQFPl4z3hWHR3ODgOWJeQK8qbhUQYDKsylDIJpBLrOaZYo4ZGHc5caUp2UWWXKGUVVqBBx+BiL4evMzWnFwJKdpF/1TPAFb881x9dfNUoqazD5K8S8MHujFY24rXuZADHcXjprk74ZGIk7OT6pSqjVhzCgVZW9lj3LAAxIe7Y/nx/hHk7obiiFg9/Ho8Pf89AXUPL71pbc/IXAFzsFVg3Lca4gcEHv2fi0f8l4ApdyBATsaIcBwDgP7398cMzfeCtVuHCtUrcvfIIvjyc3aYksJVNgVEnbyf88lx/RAe5oaK2AdO+PYH5P50RRRsAlVyK5Q/3wKK7wyFt6vk5asUhHMu+3qbXtba/B4Mn+gfj80d7wUmp3+F6zCeH25T8JOROUHP6f2eqY0xWUQWq6xvhpJQhxMPBNC9KrIphCWO4j9qqbljZMkp2WZA1f+UNS7gm9PYHY/olK/esPIKUvFK+h2ZR93b3xdZn+yLEwwGF5bWYuvY45v10BmXVbVu6Y00C3R2w9dm+GNfDFzoGrNx3HuM//UNUjYmVMineHR+BpfdHQCWX4OiF6xj18SFsOp5HVV5ElCIDXLH9+f4YEOqB2gYd3tl5FhPWHGt1bztb+OvxdFJi3bQY41K2dQm5GL3icCuSPtY7CxzH4Yn+wVj/VAz8XOyQV1KNCWuO4fWfU1FW1bo4ae03RgDgri7e2PFCf0T4OaO0qh7Tvj2BFzYk45q2lu+hEZGoMia7aAmjuRmuiSLaOUNCVXRWxVT/tQw7MVK/LuGgZJcF2MD5GgD9XdulD3TDp5N6wt1BgczCCoz/9A+8tvVMi0/cbCHJ3dlHjZ0vDDA2Z16fkIuhHxzAxsTcFvfysvZpsFfIsHxCJFY90hOu9nKkXS3HPSuPYP5PZ1DSwuWd1j4HADAhOgC/zRqIngEu0NY24JUtp3H/Z0dFlwQmpmHtscLTSYlvn4jG4vFdYa+QIjGnBKNXHMbrP6eKpmG7gVwqwaJ7wrHuqRj4OKuMO7k+vyEZ+WUtqwK15mNkTIg7ds0egAm9/QEA38RfwpAPD+D7Y5dE178q0N0BP86IxdMDQyDhgF9SrmL4RwfxbXxOm3e6JuTfGJJd1Jz+37W1z2BKU3N6WsJovdraKzLdmOyifl1CQcku0mpjInywZ84g3NdU2bMhMReDl+3Hir1Zt71zawt3aG9mp5DijXu7YP20GLT3dMD1yjrM3XoGYz85jF/P5N+2usfWdmUa280Hu18ciLHdfKBj+goG43fhNtVutjUDQLCHAzZP74t5Y8LgoJDiVF4p7lv1B55bfxIZBeLsXUTEi+M4TIoJxO7ZAzGyiwaNOoZv4i9hwHv78eHvGS3udWgLN0YAoF8HD+yaPRCPxASA44DtKVcx5IMDWLwzHUVa297kw0klx9IHuuH7J2PQwcsRJZV1WLAtFSM+PoQfky63ONFjC18FpUyKeWM64+eZ+pYQZdX1WPRzGkZ8dBC/pFwV3aY3xHIMS6gdKNlldqeoOb2oMcaM5/2dfaiySygo2WVBtnLyDui3G18xIRKbp8eieztnVNY14uO9mei7NA5Lfj0rmp36+rbXX8gsGNsZTkoZzhVo8ey6kxi14hC2nryMmvrW9DWzTl5OKqx6pCc2Pq3ffr68pgEf781E/6X78MHuDBSU2f53QSrh8PTA9tj/f4Nxf08/AMCO0/kYufwQpn+XhKRLJTaX6CTmYwuhwt/NHp9PjsK6p2IQ5u0EbW0DVu47j/7v7cM7O9Jxsbjylr9ni38nznZyvDs+Atuf64/eQa6oqdfhi8MXMeC9/Xjjl7S/zYWtTUH/UA/8NmsA3rgnHC72clwsrsT/bU7B0A8P4H9HLt7+JpmFx2kJEe2c8fPMfnj7vi7wcFQi53oVXtiQjGEfHsB3xy4Z+ysRYipU2fXvTHF9VtvQaNycJaIdVfVYG1N8BwrKa6CtbYBUwiGYerYJBiW7LMAWT94Nege54adn+2HlxEiEeTuhsq4Rnx/KRt+l+/DUNyewN72w2ZIFziYu45qTSyV4akAIDr86BC8M7QAnpQyZhRWYsykFfZfuw5Jfz+LCtb9sQW9704A+Ie7Y8Xx/rJwYiY4aR2hrG/Df/efR7719eOa7EziYea35d8EG58BLrcJH/+mBX18YgDER3gCAXWkFeOCzeIxecRjfH7vU6r41RDxsMVb06+CBX18YgM8m9URnHzUq6xrx5ZGLGPLBATz6ZQJ2ns4XxU0BAOjq54xNz8Ti68d7IzLABbUNOnx9NAdDPjiAyf9LwK7U5nNhS8dIuVSCqf2CceTVoZg7OgwejgrklVTj7R3piH53L/5vcwoSL5aIouehTCrB5NggHHx5MF4c3hHOdnLkXK/Cwm2piF0ah9d/TsWZy2U2eTwglldNPbtarC1/ceeL9LvwOdvRLnxWrQ1fgsxC/bVekLs9FDJKsQgFHflIm0kkHO7p7ou7u/lg37kifH4wG4k5Jdh7thB7zxbCw1Epil1JXOwVmHNXJzw5IATfH7uEdccu4WpZDT4/lI3PD2Uj3EcNWz+PlzZ9F8ZG+GB3WgHW/pGDxJwS7E4rxO60Qrg7KESx9XW4rxqfTuqFjAItvjicje0pV3GuQIsF21Lx5vY0DAz1xN3dfTA0TANnOznfwyXErCQSDqMjfDCqqzf2ZxThu/hLOJB5DUfOF+PI+WLYK6QY1lmDsRHexmSPrR4lOI7D4E5eGNTRE4ezirH2j4s4kHkNh7OKcTirGE5KGRqbkhy2mOtwVMowfVB7TIkNwpaTl/H9sUs4V6DFj0mX8WPSZWjUSozu6oPRXb3/TPbY6JfBQSnDrOGhmDYwGJuO5+HLIxdx+UY1vom/hG/iL6GTxgmjunpjRLgGXXxpZy9yZwzLGKmyy7zO5huWrznR36pIGSr7OmqceB4JuRkluyzI1g99HMdhWGcNhnXW4HxRBTadyMOWpMsorqhFcYV4dh5ytpNj5pAOeGZgCPZnXMP6hEs4nFWM9Pxy43NsscLtZoaL29ERPsgo0GJdwiVsT7mK6zf167HtGdDr5O2EDx7qjgVjO+PHpMvYfOIyMgq1iDtXhLhzRZBwQA9/Fwzs6IkBoR7o6ucMpYxOSEXPRv84OI7D0DANhoZpkFdShY3Hc7Et+SqulFZje8pVbE+5yvcQLYbjOAzs6ImBHT2RV1KFdQm5+PnUFeTftOzblq+X7BRSPNonEJNiApCcV4oNCbnYlVaAwvJafH00B18fzeF7iBZjr5Bhar9gTI4NwpHzxfgx6TJ2pxUgo1CLjEItVsRlwcdZhcGdPBET7I7oYDf4utjxPWxiJSqbKruoZ9ftmeKc3NCYnHo1WSdTfAeymiq7QinZJSiU7LIAG7w5+686eDli3pjO+L+7OuHohWL8dqYAR84XY0S4hu+hWYxMKsGIcA1GhGtwo7IOu9IK8OuZfDjbyeHhqOB7eBbTydsJb93XFQvvDsfRC9exI+Uqzl+rwMCOnnwPzWJc7BV4akAInhoQgqxCLbafzsevZ/JxvqgCJ3NLcTK3FMv3ZkEhlaCLnxqR/q7o7u+MTt5OCPFwpHJokRBTrPB3s8fLI8Pwf3d1QsrlMuw8fRW70gqQV6LfqdBHRBfz/m72mDs6DK+M7ISk3BvYkXIVCRdL8FAvf76HZnYcx6FngCt6BrjinfFd8cf5Yuw4nY/954pwo6oeapUMTkpxVL9KJRwGdfTEoI6eKKuqx+70AsSdLcShzGLkl9VgQ2IeNiTmAQDaudqhZ4Aruviq0cXXGV181XB1EM95BWk5WsbYcm2ppj2bT8kuW9CW87DMIn1lV6iXo2kGQ0zijo58q1atwrJly1BQUIDu3btj5cqViI6Ovu3zN2/ejIULFyInJwehoaF47733MGbMmDseNLEeCpkEgzt5YXAnL76HwitXBwUmRgdgYnQA30PhjVwqMZ7Ii1moxglzRjhhzoiOuFJajcOZ13Ao6xqOZZegpLIOybmlSM4tNT5fJuEQ5OGAjhpHBHs4oJ2rPdq52qGdqz18XVQ2VwnW2vhCrBvHcejh74Ie/i6YN6YzLhZXokhbi+ggN76HZnESCYfeQW7oLcLPDuh3LDRU/jXqGFKvlMHVXiHK5VfO9nL8J8of/4nyR019I+IvXMfRC8VIuFiC1CtluHyjGpdvVOOXm6ohNWolgtwdEOzhgEB3BwR72KOdqz28nVVws1dAYoMtBHbu3Im33noLp0+fhkqlwqBBg7Bt2zbjz3NzczFjxgzs378fjo6OmDJlCpYsWQKZTDyJH2pQb36MMZwt0Ce7winZJUqMMZxvquyiZYzC0uqj/Q8//IA5c+Zg9erViImJwfLlyzFy5EhkZGTAy+vvCY2jR49i4sSJWLJkCe6++26sX78e48aNw8mTJ9G1a1eTfAhrQWu4CSE383Oxw4ToAEyIDgBjDLklVU3Jrhs4c6UMWYUV0NY24HxRBc4XVfzt9zkOcHdQwsNRAU8nJdwdFPBwVMLDSQk3B4W+KkIlh9NN/+uolEElF+ZJb2vji62y9WXOt8NxHEI8HRHiSXdFxU4q4dDd34XvYQiCSi7FkDAvDAnTHwMrahuQdOkGUq+UIe1qGdKvliPnehUKy2tRWF6LhIslf3sNuZSDl5MKXmolvNUquDsq4GKngIu9HM52crjY6//dxU4OtZ0c9gop7BUyQffY3LJlC6ZNm4Z3330XQ4cORUNDA1JTU40/b2xsxNixY+Ht7Y2jR48iPz8fjz32GORyOd59910eR25Z1fX6nl32Ao37QtDWy7OC8hqUVtVDKuHQgap6rJIpvgPa2gbIaCdGwWl1suujjz7CtGnT8PjjjwMAVq9ejZ07d+Krr77C3Llz//b8FStWYNSoUXj55ZcBAG+//Tb27NmD//73v1i9enUbh98yZy6XWeR9bod2XyOE/BuO4xDorr8jPy7SD4D+TlF+WQ0yC7XIKqxAbkkVLt+oMt7Vr65vNPbEO1egbfF7KaQSOCj1FzMvDOuAh3sLo+KwtfHFlEqr6oxL6Phiiw3JCSGm5aiU/a1KWltTj/NFFbh0vQoXiytx6XolLl6vwpUb1bheWYv6RoYrpdW4Utq6Y5ydXGqMFT880wc+zsJYXtzQ0IBZs2Zh2bJlePLJJ42Ph4eHG//9999/R3p6Ovbu3QuNRoMePXrg7bffxquvvoo33ngDCoX5l3026pixlxNfrpbq+wDaK8VTzXanLhRVQCFtfduI5LwbAID2ng6CvZlIWib7WgXUqtYvnU+5XAoACPJwoNYjAtOqI19dXR2SkpLw2muvGR+TSCQYPnw44uPjb/k78fHxmDNnTrPHRo4c2azM+K9qa2tRW/tnQ/Py8rYFinv+e6RNv28qVNhFCGkNjuPg62IHXxe7vy0FZozhemUdCstrUFxRh+tNSa/iijoUa2tRUlUHbU0DtDX1qKhp0P97rf4Ob12jDnVVOtyoqkdtg46Pj/Y3rY0vpo4TR84X47n1yW16DVOhWEEIaQ0nlRyRAa6IDHD928/qG3W4pq1FQXkNisprUFBWg5LKOpRW16Osuh6lVfX6f6/SP1ZeXW/cObq6vhHV9Y0A6u4oCWAuJ0+exJUrVyCRSBAZGYmCggL06NEDy5YtM64aiY+PR0REBDSaP3vFjhw5EjNmzEBaWhoiIyNv+dqmjC21DY2CuQaxp2WMt2UIua9sOd2m16F+XdZv4c9pbfp96tclPK1KdhUXF6OxsbFZ4AAAjUaDc+fO3fJ3CgoKbvn8goKC277PkiVL8Oabb7ZmaP/I11llste6U64OCgwMFXevIkKI6XAcp1+y6Khs8e/odAwVdfrEV1VtA6rqGuHjwv/xEWh9fDF1nLCTSwURK3oFucGdGk0TQkxELpUYb5q0BGMMtQ06VDbFiIraBlTVNcDZTjgbBWRnZwMA3njjDXz00UcICgrChx9+iMGDByMzMxNubm63vf4AYLFrEA6cIOKKu6NSVJsCtdYjMQH45uglsDaUV6sUUkwQSJU8ab2J0QFYcyi7bd8BuVTUvZmFSpA1ra+99lqzarDy8nL4+9/5rkRHXxtmimERQohVk0g4qFXyOyrRFhpTx4lhnTUY1lk8u8USQsitcBwHlVwKlVwKdwu/99y5c/Hee+/943POnj0LnU5fkTx//nw88MADAIC1a9eiXbt22Lx5M5555pk7HoMpY4udQkrXIFbg6YHt8fTA9nwPg/BoSt8gTOkbxPcwiBm0Ktnl4eEBqVSKwsLCZo8XFhbC29v7lr/j7e3dqucDgFKphFLZ8moFQggh1q218YXiBCGE2JaXXnoJU6dO/cfnhISEID8/H0DzHl1KpRIhISHIzc0FoL/+SExMbPa7hvhC1yCEECIOrVqAr1Ao0KtXL8TFxRkf0+l0iIuLQ2xs7C1/JzY2ttnzAWDPnj23fT4hhBDxuZP4QgghxHZ4enoiLCzsH/8xxAqlUomMjAzj79bX1yMnJweBgYEA9NcfZ86cQVFRkfE5e/bsgVqtbpYkI4QQYrtavYxxzpw5mDJlCqKiohAdHY3ly5ejsrLSuHvWY489Bj8/PyxZsgQAMGvWLAwaNAgffvghxo4di40bN+LEiRNYs2aNaT8JIYQQq/Zv8YUQQghRq9WYPn06Xn/9dfj7+yMwMBDLli0DADz00EMAgLvuugvh4eGYPHky3n//fRQUFGDBggWYOXMmVW4RQohItDrZ9fDDD+PatWtYtGiRcfeTXbt2GZs+5ubmQiL5s2Csb9++WL9+PRYsWIB58+YhNDQU27ZtM+6WQgghhAD/Hl8IIYQQAFi2bBlkMhkmT56M6upqxMTEYN++fXB11e9IKZVKsWPHDsyYMQOxsbFwcHDAlClT8NZbb/E8ckIIIZbCsbZsO2Ah5eXlcHZ2RllZGdRq2taVEEKEQEjHZiGNhRBCiJ61H5utffyEEGKLWnpsblXPLkIIIYQQQgghhBBChIySXYQQQgghhBBCCCHEZlCyixBCCCGEEEIIIYTYDEp2EUIIIYQQQgghhBCbQckuQgghhBBCCCGEEGIzZHwPoCUMG0aWl5fzPBJCCCEGhmOyEDb1pThBCCHCI6Q4cScothBCiPC0NLZYRbJLq9UCAPz9/XkeCSGEkL/SarVwdnbmfQwAxQlCCBEiIcSJO0GxhRBChOvfYgvHrOBWi06nw9WrV+Hk5ASO41r9++Xl5fD390deXh7UarUZRih8NAd6NA80BwDNgUFb54ExBq1WC19fX0gk/K6Kb2ucAOh7AdAcADQHBjQPNAeAbcWJO0HXIG1Hc6BH80BzANAcGFgqtlhFZZdEIkG7du3a/DpqtVrUXyqA5sCA5oHmAKA5MGjLPAjlTr2p4gRA3wuA5gCgOTCgeaA5AGwjTtwJugYxHZoDPZoHmgOA5sDA3LHF+m6xEEIIIYQQQgghhBByG5TsIoQQQgghhBBCCCE2QxTJLqVSiddffx1KpZLvofCG5kCP5oHmAKA5MKB5aI7mg+YAoDkwoHmgOQBoDtqK5o/mwIDmgeYAoDkwsNQ8WEWDekIIIYQQQgghhBBCWkIUlV2EEEIIIYQQQgghRBwo2UUIIYQQQgghhBBCbAYluwghhBBCCCGEEEKIzaBkFyGEEEIIIYQQQgixGZTsIoQQQgghhBBCCCE2QxTJrlWrViEoKAgqlQoxMTFITEzke0hms2TJEvTu3RtOTk7w8vLCuHHjkJGR0ew5NTU1mDlzJtzd3eHo6IgHHngAhYWFPI3Y/JYuXQqO4zB79mzjY2KYgytXruDRRx+Fu7s77OzsEBERgRMnThh/zhjDokWL4OPjAzs7OwwfPhxZWVk8jtj0GhsbsXDhQgQHB8POzg7t27fH22+/jZs3obW1eTh06BDuuece+Pr6guM4bNu2rdnPW/J5S0pKMGnSJKjVari4uODJJ59ERUWFBT+F5VGcoDghxjgBUKwQY5wAKFZYgpjiCkCx5VbEGlvEHlcAccYWQcYVZuM2btzIFAoF++qrr1haWhqbNm0ac3FxYYWFhXwPzSxGjhzJ1q5dy1JTU9mpU6fYmDFjWEBAAKuoqDA+Z/r06czf35/FxcWxEydOsD59+rC+ffvyOGrzSUxMZEFBQaxbt25s1qxZxsdtfQ5KSkpYYGAgmzp1KktISGDZ2dls9+7d7Pz588bnLF26lDk7O7Nt27axlJQUdu+997Lg4GBWXV3N48hNa/Hixczd3Z3t2LGDXbx4kW3evJk5OjqyFStWGJ9ja/Pw66+/svnz57OtW7cyAOynn35q9vOWfN5Ro0ax7t27s2PHjrHDhw+zDh06sIkTJ1r4k1gOxQmKE2KME4xRrGBMnHGCMYoV5ia2uMIYxZa/EmtsobiiJ8bYIsS4YvPJrujoaDZz5kzj/29sbGS+vr5syZIlPI7KcoqKihgAdvDgQcYYY6WlpUwul7PNmzcbn3P27FkGgMXHx/M1TLPQarUsNDSU7dmzhw0aNMgYaMQwB6+++irr37//bX+u0+mYt7c3W7ZsmfGx0tJSplQq2YYNGywxRIsYO3Yse+KJJ5o9dv/997NJkyYxxmx/Hv4aaFryedPT0xkAdvz4ceNzfvvtN8ZxHLty5YrFxm5JFCcoTogxTjBGsYIxihOMUawwB7HHFcYotog1tlBc0RN7bBFKXLHpZYx1dXVISkrC8OHDjY9JJBIMHz4c8fHxPI7McsrKygAAbm5uAICkpCTU19c3m5OwsDAEBATY3JzMnDkTY8eObfZZAXHMwS+//IKoqCg89NBD8PLyQmRkJL744gvjzy9evIiCgoJmc+Ds7IyYmBibmQMA6Nu3L+Li4pCZmQkASElJwZEjRzB69GgA4pkHg5Z83vj4eLi4uCAqKsr4nOHDh0MikSAhIcHiYzY3ihMUJ8QaJwCKFQDFiVuhWNE2FFf0KLaIM7ZQXNGj2NIcX3FF1rZhC1txcTEaGxuh0WiaPa7RaHDu3DmeRmU5Op0Os2fPRr9+/dC1a1cAQEFBARQKBVxcXJo9V6PRoKCggIdRmsfGjRtx8uRJHD9+/G8/E8McZGdn47PPPsOcOXMwb948HD9+HC+88AIUCgWmTJli/Jy3+tuwlTkAgLlz56K8vBxhYWGQSqVobGzE4sWLMWnSJAAQzTwYtOTzFhQUwMvLq9nPZTIZ3NzcbHJOKE5QnBBrnAAoVgAUJ26FYkXbiD2uABRbxBxbKK7oUWxpjq+4YtPJLrGbOXMmUlNTceTIEb6HYlF5eXmYNWsW9uzZA5VKxfdweKHT6RAVFYV3330XABAZGYnU1FSsXr0aU6ZM4Xl0lrNp0yasW7cO69evR5cuXXDq1CnMnj0bvr6+opoHQm6H4oR44wRAsQKgOEGIOVBsEW9sobiiR7FFGGx6GaOHhwekUunfdrgoLCyEt7c3T6OyjOeeew47duzA/v370a5dO+Pj3t7eqKurQ2lpabPn29KcJCUloaioCD179oRMJoNMJsPBgwfxySefQCaTQaPR2Pwc+Pj4IDw8vNljnTt3Rm5uLgAYP6et/228/PLLmDt3LiZMmICIiAhMnjwZL774IpYsWQJAPPNg0JLP6+3tjaKiomY/b2hoQElJiU3OCcUJihNijRMAxQqA4sStUKxoGzHHFYBii9hjC8UVPYotzfEVV2w62aVQKNCrVy/ExcUZH9PpdIiLi0NsbCyPIzMfxhiee+45/PTTT9i3bx+Cg4Ob/bxXr16Qy+XN5iQjIwO5ubk2MyfDhg3DmTNncOrUKeM/UVFRmDRpkvHfbX0O+vXr97ftnjMzMxEYGAgACA4Ohre3d7M5KC8vR0JCgs3MAQBUVVVBIml+mJNKpdDpdADEMw8GLfm8sbGxKC0tRVJSkvE5+/btg06nQ0xMjMXHbG4UJyhOiDVOABQrAIoTt0Kxom3EGFcAii0AxRaA4ooBxZbmeIsrd9TW3ops3LiRKZVK9vXXX7P09HT29NNPMxcXF1ZQUMD30MxixowZzNnZmR04cIDl5+cb/6mqqjI+Z/r06SwgIIDt27ePnThxgsXGxrLY2FgeR21+N++Ewpjtz0FiYiKTyWRs8eLFLCsri61bt47Z29uz77//3vicpUuXMhcXF/bzzz+z06dPs/vuu8+qt7u9lSlTpjA/Pz/jtr9bt25lHh4e7JVXXjE+x9bmQavVsuTkZJacnMwAsI8++oglJyezS5cuMcZa9nlHjRrFIiMjWUJCAjty5AgLDQ216e3kKU5QnGBMfHGCMYoVjIkzTjBGscLcxBZXGKPYcjtiiy0UV/TEGFuEGFdsPtnFGGMrV65kAQEBTKFQsOjoaHbs2DG+h2Q2AG75z9q1a43Pqa6uZs8++yxzdXVl9vb2bPz48Sw/P5+/QVvAXwONGOZg+/btrGvXrkypVLKwsDC2Zs2aZj/X6XRs4cKFTKPRMKVSyYYNG8YyMjJ4Gq15lJeXs1mzZrGAgACmUqlYSEgImz9/PqutrTU+x9bmYf/+/bc8BkyZMoUx1rLPe/36dTZx4kTm6OjI1Go1e/zxx5lWq+Xh01gOxQmKE2KME4xRrBBjnGCMYoUliCmuMEax5XbEGFvEHlcYE2dsEWJc4Rhj7M5qwgghhBBCCCGEEEIIERab7tlFCCGEEEIIIYQQQsSFkl2EEEIIIYQQQgghxGZQsosQQgghhBBCCCGE2AxKdhFCCCGEEEIIIYQQm0HJLkIIIYQQQgghhBBiMyjZRQghhBBCCCGEEEJsBiW7CCGEEEIIIYQQQojNoGQXIYQQQgghhBBCCLEZlOwihBBCCCGEEEIIITaDkl2EEEIIIYQQQgghxGZQsosQQgghhBBCCCGE2Iz/B9VwlBkgpJmcAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "cb054cdfc7c1803b",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### AMPA NMDA"
   ]
  },
  {
   "cell_type": "code",
   "id": "9cb9134596853779",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:52.359524Z",
     "start_time": "2025-10-06T03:56:52.354013Z"
    }
   },
   "source": [
    "class AMPA_NMDA(bp.Projection):\n",
    "  def __init__(self, pre, post, delay, prob, g_max, E=0.):\n",
    "    super().__init__()\n",
    "    \n",
    "    self.proj = bp.dyn.ProjAlignPreMg2(\n",
    "      pre=pre, \n",
    "      delay=delay, \n",
    "      syn=AMPA.desc(post.num),\n",
    "      comm=MaskedLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),\n",
    "      out=MgBlock(E=E),\n",
    "      post=post, \n",
    "    )"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "d85b04601fa90c9f",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:56:52.807711Z",
     "start_time": "2025-10-06T03:56:52.376813Z"
    }
   },
   "source": [
    "run_a_net(SimpleNet(AMPA_NMDA))"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\mixin.py:399: UserWarning: Please use \".sum_current_inputs()\" instead. \".sum_inputs()\" will be removed.\n",
      "  warnings.warn('Please use \".sum_current_inputs()\" instead. \".sum_inputs()\" will be removed.', UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "abe226bff1e048d683a8f80b894b0367"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x350 with 3 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLsAAAFpCAYAAAB0yCp4AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAA6epJREFUeJzsnXd4VGXa/z8zk2TSC6kQAiH03kUEBV0UXfu6rr2LFfVV113Z31rWXUXWVXlfV8WGYEVdXdvaEay0FUFBOiRASAIB0sskmfP7Y3oySWaSmcwp9+e6cgUyZ855npzJc57n+9z39zYpiqIgCIIgCIIgCIIgCIIgCDrAHOkGCIIgCIIgCIIgCIIgCEKoELFLEARBEARBEARBEARB0A0idgmCIAiCIAiCIAiCIAi6QcQuQRAEQRAEQRAEQRAEQTeI2CUIgiAIgiAIgiAIgiDoBhG7BEEQBEEQBEEQBEEQBN0gYpcgCIIgCIIgCIIgCIKgG0TsEgRBEARBEARBEARBEHSDiF2CIAiCIAiCIAiCIAiCbhCxSxBUwJVXXkl+fn6kmyEIgiAIgiAIgiAImkfELqHH+Pnnn/ntb39L//79iY2NJTc3l5NPPpknnngi0k3TPd9//z33338/FRUVkW6KIAhC2JDnTPh56qmnWLJkSaSbIQiCIAiC0CEmRVGUSDdC0D/ff/89J554Iv369eOKK64gJyeHffv2sXr1anbt2sXOnTsj3cSIcuWVV7Jy5UoKCwvDcv5//OMf3HXXXezZs0ciyARB0CXynOkZRo0aRUZGBitXrox0UwRBEARBENolKtINEIzBgw8+SEpKCuvWrSM1NdXntYMHD0amUYIgCIJuMOJzpq6ujvj4+DY/b25uxm63ExMTE4FWCYIgCIIgRB5JYxR6hF27djFy5Mg2CxCArKws979nzJjB2LFj/Z5j6NChzJ49G4DCwkJMJhP/+Mc/ePbZZxk4cCBWq5XJkyezbt26gNpUUVHB7bffTn5+Plarlb59+3L55ZdTXl7uPubgwYNcc801ZGdnExsby9ixY1m6dKnPeYJty7vvvsuoUaOIjY1l1KhR/Pvf/25zzMqVKzGZTG12zl3Xap1CsnXrVn73u9+RmZlJXFwcQ4cO5f/9v/8HwP33389dd90FwIABAzCZTJhMJncU2YsvvshJJ51EVlYWVquVESNG8PTTT7dpU35+PmeccQbffvstxxxzDLGxsRQUFPDSSy916Xfb2NjIfffdx6BBg7BareTl5fGHP/yBxsbGNucTBEHoDC0+Z5YsWeIzHrvw9wyYOXMmo0aN4ocffuCEE04gPj6eP/3pTz7tXLhwobudv/zyC+B4Pvz2t7+lV69exMbGMmnSJN5//32f67na8d1333HHHXeQmZlJQkIC5557LocOHXIfl5+fz+bNm/nqq6/cz5KZM2cG9LsQBEEQBEHoSSSyS+gR+vfvz6pVq9i0aROjRo1q97jLLruMOXPmtDlu3bp1bN++nT//+c8+x7/22mtUV1dz/fXXYzKZ+Pvf/85vfvMbdu/eTXR0dLvXqamp4fjjj2fLli1cffXVTJgwgfLyct5//332799PRkYG9fX1zJw5k507dzJ37lwGDBjAW2+9xZVXXklFRQW33XZb0G357LPPOO+88xgxYgTz58/n8OHDXHXVVfTt27crv1YAfvrpJ44//niio6O57rrryM/PZ9euXXzwwQc8+OCD/OY3v2H79u28/vrrPP7442RkZACQmZkJwNNPP83IkSM566yziIqK4oMPPuCmm27Cbrdz8803+1xr586d/Pa3v+Waa67hiiuuYPHixVx55ZVMnDiRkSNHBvy7tdvtnHXWWXz77bdcd911DB8+nJ9//pnHH3+c7du38+6773b59yEIgjHR4nMmWA4fPsxpp53GhRdeyKWXXkp2drb7tRdffJGGhgauu+46rFYrvXr1YvPmzUybNo3c3FzuvvtuEhISePPNNznnnHN4++23Offcc33Of8stt5CWlsZ9991HYWEhCxcuZO7cubzxxhsALFy4kFtuuYXExET3hop3GwRBEARBEFSDIgg9wGeffaZYLBbFYrEoU6dOVf7whz8on376qWKz2XyOq6ioUGJjY5U//vGPPj+/9dZblYSEBKWmpkZRFEXZs2ePAijp6enKkSNH3Me99957CqB88MEHHbbn3nvvVQDlnXfeafOa3W5XFEVRFi5cqADKK6+84n7NZrMpU6dOVRITE5Wqqqqg2zJu3Dild+/eSkVFhc/vBlD69+/v/tmKFSsUQFmxYoVP21zXevHFF90/O+GEE5SkpCSlqKjIbz8URVEeeeQRBVD27NnTpr91dXVtfjZ79myloKDA52f9+/dXAOXrr792/+zgwYOK1WpV7rzzTvfPAvndvvzyy4rZbFa++eYbn9cXLVqkAMp3333X5r2CIAgdocXnzIsvvuh3bPb3DJgxY4YCKIsWLfI51tXO5ORk5eDBgz6v/epXv1JGjx6tNDQ0+Fz7uOOOUwYPHuz+masds2bN8nl23H777YrFYvF5Zo0cOVKZMWNGh30XBEEQBEGINJLGKPQIJ598MqtWreKss85i48aN/P3vf2f27Nnk5ub6pFOkpKRw9tln8/rrr6M4aye0tLTwxhtvcM4555CQkOBz3gsuuIC0tDT3/48//ngAdu/e3WF73n77bcaOHdtmVxvAZDIB8NFHH5GTk8NFF13kfi06Oppbb72Vmpoavvrqq6DaUlJSwoYNG7jiiitISUnx+d2MGDGiw/a2x6FDh/j666+5+uqr6devn99+dEZcXJz735WVlZSXlzNjxgx2795NZWWlz7EjRoxw9wsc0WFDhw71+X0H8rt96623GD58OMOGDaO8vNz9ddJJJwGwYsWKgNouCILgQovPmWCxWq1cddVVfl8777zz3BG7AEeOHOHLL7/kd7/7HdXV1e5x9vDhw8yePZsdO3ZQXFzsc47rrrvOp23HH388LS0tFBUVdam9giAIgiAIkULELqHHmDx5Mu+88w5Hjx5l7dq1zJs3j+rqan7729+6vUUALr/8cvbu3cs333wDwBdffEFZWRmXXXZZm3O2FnhcC5KjR4922JZdu3Z1mOYCUFRUxODBgzGbff9Mhg8f7n49mLa4jh88eHCbaw0dOrTDtrSHa7HVWV864rvvvmPWrFkkJCSQmppKZmYmf/rTnwDaiF2t+wiOfnr/vgP53e7YsYPNmzeTmZnp8zVkyBBAv2bSgiCEF609Z4IlNze3XdP5AQMG+Px/586dKIrCPffc02asve+++4C2Y21X+yoIgiAIgqA2xLNL6HFiYmKYPHkykydPZsiQIVx11VW89dZb7sn37Nmzyc7O5pVXXuGEE07glVdeIScnh1mzZrU5l8Vi8XsN1259TxLKtrS369/S0hL0uTpi165d/OpXv2LYsGE89thj5OXlERMTw0cffcTjjz+O3W73OT5UfbTb7YwePZrHHnvM7+t5eXlBnU8QBMEbrTxngh3rvSNxO3vNNX7//ve/d5vut2bQoEE+/1fTM1UQBEEQBKE7iNglRJRJkyYBjhQ/FxaLhYsvvpglS5awYMEC3n33XebMmdPuJLwrDBw4kE2bNnV4TP/+/fnpp5+w2+0+0V1bt251vx4MruN37NjR5rVt27b5/N+1m15RUeHz89bRZAUFBQCd9qW9BdUHH3xAY2Mj77//vs+OfnfSCAP53Q4cOJCNGzfyq1/9qsvpPIIgCIGg5udMoGN9V3A9H6Kjo/2KeF1FxmxBEARBELSApDEKPcKKFSv87gx/9NFHQNs0vssuu4yjR49y/fXXU1NTw6WXXhrS9px33nls3LiRf//7321ec7Xz17/+NaWlpe4qVADNzc088cQTJCYmMmPGjKCu2bt3b8aNG8fSpUt90gM///xzn/QacAhjFouFr7/+2ufnTz31lM//MzMzOeGEE1i8eDF79+712w/A7UHTekHlWth5H1tZWcmLL74YVN+8CeR3+7vf/Y7i4mKee+65NsfU19dTW1vb5esLgmBMtPicGThwIIDPWN/S0sKzzz7b7etnZWUxc+ZMnnnmGR+hz8WhQ4e6dN6EhIQ2zxJBEARBEAS1IZFdQo9wyy23UFdXx7nnnsuwYcOw2Wx8//33vPHGG+Tn57cx3B0/fjyjRo1yG5lPmDAhpO256667+Ne//sX555/P1VdfzcSJEzly5Ajvv/8+ixYtYuzYsVx33XU888wzXHnllfzwww/k5+fzr3/9i++++46FCxeSlJQU9HXnz5/P6aefzvTp07n66qs5cuQITzzxBCNHjqSmpsZ9XEpKCueffz5PPPEEJpOJgQMH8uGHH/r1svq///s/pk+fzoQJE7juuusYMGAAhYWF/Oc//2HDhg0ATJw4EYD/9//+HxdeeCHR0dGceeaZnHLKKcTExHDmmWe6F3zPPfccWVlZfhdHofrdXnbZZbz55pvccMMNrFixgmnTptHS0sLWrVt58803+fTTT93RGIIgCIGgxefMyJEjOfbYY5k3bx5HjhyhV69eLFu2jObm5pC04cknn2T69OmMHj2aOXPmUFBQQFlZGatWrWL//v1s3Lgx6HNOnDiRp59+mr/97W8MGjSIrKwsd3ERQRAEQRAE1RCBCpCCAfn444+Vq6++Whk2bJiSmJioxMTEKIMGDVJuueUWpayszO97/v73vyuA8tBDD7V5zVVq/ZFHHmnzGqDcd999nbbp8OHDyty5c5Xc3FwlJiZG6du3r3LFFVco5eXl7mPKysqUq666SsnIyFBiYmKU0aNHKy+++GK32vL2228rw4cPV6xWqzJixAjlnXfeUa644gqlf//+PscdOnRIOe+885T4+HglLS1Nuf7665VNmzYpQJs2bNq0STn33HOV1NRUJTY2Vhk6dKhyzz33+Bzz17/+VcnNzVXMZrNPqfv3339fGTNmjBIbG6vk5+crCxYsUBYvXuxzjKIoSv/+/ZXTTz+9TR9nzJjRpgx9IL9bm82mLFiwQBk5cqRitVqVtLQ0ZeLEicpf/vIXpbKyss11BEEQOkKrz5ldu3Yps2bNUqxWq5Kdna386U9/Uj7//HMFUFasWOE+bsaMGcrIkSODaqfr/JdffrmSk5OjREdHK7m5ucoZZ5yh/Otf/3If8+KLLyqAsm7dOp/3rlixok07SktLldNPP11JSkpSgDbjvyAIgiAIghowKYq4jgrq5H//93+5/fbbKSws9FsFUBAEQRC6gzxnBEEQBEEQ9ImIXYIqURSFsWPHkp6e3i2zdEEQBEHwhzxnBEEQBEEQ9It4dgmqora2lvfff58VK1bw888/895770W6SYIgCIKOkOeMIAiCIAiC/pHILkFVFBYWMmDAAFJTU7npppt48MEHI90kQRAEQUfIc0YQBEEQBEH/iNglCIIgCIIgCIIgCIIg6AZzpBsgCIIgCIIgCIIgCIIgCKFCxC5BEARBEARBEARBEARBN+jCoN5ut3PgwAGSkpIwmUyRbo4gCIJuURSF6upq+vTpg9msvv0SeR4IgiCEH7U/CyKBPH8EQRB6hkCfQboQuw4cOEBeXl6kmyEIgmAY9u3bR9++fSPdjDbI80AQBKHnUOuzIBLI80cQBKFn6ewZpAuxKykpCXB0Njk5OcKtEQRB0C9VVVXk5eW5x121Ic8DQRCE8KP2Z0EkkOePIAhCzxDoM0gXYpcrVDg5OVkeLoIgCD2AWlM05HkgCILQc6j1WRAJ5PkjCILQs3T2DJIke0EQBEEQBEEQBEEQBEE3iNglCIIgCIIgCIIgCIIg6AYRuwRBEARBEARBEARBEATdoAvPLkEQBEEQBEEQBDWjKArNzc20tLREuim6xmKxEBUVJZ5ygmBwROwSBEEQBEEQBEEIIzabjZKSEurq6iLdFEMQHx9P7969iYmJiXRTBEGIECJ2CYIgCIIgCIIghAm73c6ePXuwWCz06dOHmJgYiToKE4qiYLPZOHToEHv27GHw4MGYzeLcIwhGRMQuQRAEQRAEQRCEMGGz2bDb7eTl5REfHx/p5uieuLg4oqOjKSoqwmazERsbG+kmCYIQAUTmFgRBEARBEARBCDMSYdRzyO9aEAQZBQRBEARBEARBEARBEATdIGKXEHG++KWMq15cy6Hqxkg3pcf5oegosx77im92HIp0U3qc8ppGTl34NS98uyfSTREEQSXUNjZHugkRwW5XuOfdTSxbuzfSTYkId7/9E5e9sIYWuxLppgiCIAhC0NjtCv/6YT8XPbuaSX/7guP//iW3vv4ja/cciXTTDI2IXULEufal/7Ji2yHuf39zpJvS41z/8n/ZebCGy15YG+mm9DjPfbObraXV/PXDXyLdFEEQVMD6vUcZed+nhnwWrNp9mJdXF3H3Oz9HuikRYdm6fXyzo5z/FsqiQBAEQdAWR2ptXPjsan7/1kZW7T5MeU0j+47U8/7GA/zumVXMfW091Q1NkW6mIRGxS1ANuw7VRLoJPU5jsz3STYgYZqlCJAiCF499th2AJd8XRrYhEaCpxfMssBs4uqmiXhYDgqA2rrzySkwmEyaTiZiYGAYNGsQDDzxAc3P3I3FXrlyJyWSioqKi3WPefvttLBYLxcXFfl8fPHgwd9xxR7fbIghdobK+iQufXcXawiMkxFi4a/ZQPpg7ndfmTOGiY/Iwm+DDn0o496nvKa6oj3RzDYeIXYJqqDLgJDc5NjrSTYgYSbFSDFYQBA8pccYdD5O9+l5t0FROMOY8QBC0wKmnnkpJSQk7duzgzjvv5P777+eRRx7pkWufddZZpKens3Tp0javff311+zcuZNrrrmmR9oiCN4oisL/LPuR7WU1ZCdbeW/uNG4+cRCj+6Zw3MAM5v9mDO/cNI2c5Fh2Hqzh4udWU1bVEOlmGwoRuwTVUNVgvAl+spEXd15Cn3dUgyAIxsTIAni0V9WwyjrjCj5GnAcIxkVRFOpszRH5UpTgIkitVis5OTn079+fG2+8kVmzZvH+++8DcPToUS6//HLS0tKIj4/ntNNOY8eOHe73FhUVceaZZ5KWlkZCQgIjR47ko48+orCwkBNPPBGAtLQ0TCYTV155ZZtrR0dHc9lll7FkyZI2ry1evJgpU6YwcuTIoPojCKHgnfXFrNh2CGuUmReumMygrKQ2x4zLS+Wdm44jr1ccRYfruHbpf2loaolAa42JcWeWKqOxuYUGm52UeOOKHzUG3M1ONvDiznthW1XfRHqiNYKtEQQh0hhZ/Ld7LTwr6m30Iz6CrelZvBfdEtklGIn6phZG3PtpRK79ywOziY/p+hw0Li6Ow4cPA440xx07dvD++++TnJzMH//4R37961/zyy+/EB0dzc0334zNZuPrr78mISGBX375hcTERPLy8nj77bc577zz2LZtG8nJycTFxfm93jXXXMNjjz3G119/zQknnABATU0N//rXv3j88ce73A9B6CqVdU387T8O3+HbZg1mVG5Ku8f2SY3jtWuP5ax/fsvPxZX86d8/8+j5YzGJpUvYkcgulfDr//2GsQ98ZsiKhEYmycBpjN4DvPi0CILgLf43Nhtr19Nb7Ko02HjobVFWJQa+gqBqFEXhiy++4NNPP+Wkk05yi1zPP/88xx9/PGPHjuXVV1+luLiYd999F4C9e/cybdo0Ro8eTUFBAWeccQYnnHACFouFXr16AZCVlUVOTg4pKf4FgxEjRnDssceyePFi98/efPNNFEXhwgsvDHu/BaE1L3y3h6N1TQzJTmTO8QWdHp/XK54nL56AxWzinfXFvLfhQA+0UjBuWInK2HWoFoCV2w5y/qS8CLdG6CmS4zx/gk0tdqItxtGfvXfzKwyctiMI3jS32NlSUs2IPslYzMba8Uu0esbDyvomspIsEWxNz+KdUGS08dDuE9llvAhvwbjERVv45YHZEbt2MHz44YckJibS1NSE3W7n4osv5v7772f58uVERUUxZcoU97Hp6ekMHTqULVu2AHDrrbdy44038tlnnzFr1izOO+88xowZE3Sbr776am6//XaeeOIJkpKSWLx4Meeffz5JSW1TxwQhnFQ1NPHid3sA+J9ZQwJevx03KIP/+dVgHv18O/e9v5mpA9PJTo4NZ1MNj3FW1hrBiH4VUQZb0Hnj7VtltN18b7sISV0RBAf3f7CZM//5LQu/2B7ppkQUo/lW+Yj/BhsPFYnsEgyKyWQiPiYqIl/Bpk+deOKJbNiwgR07dlBfX8/SpUtJSEgI6L3XXnstu3fv5rLLLuPnn39m0qRJPPHEE0H/vlwRXG+++SY7duzgu+++E2N6ISK8vmYv1Q3NDM5K5NSROUG998aZAxmdm0JlfRN/eufnoP3zhOAQsUtlGHHRb2RTYm+MJna19qgRBAFeWb0XgCe+3BnhlvQ83ulsxhsPPf+urDPWeGgXzy5BUD0JCQkMGjSIfv36ERXlmbcPHz6c5uZm1qxZ4/7Z4cOH2bZtGyNGjHD/LC8vjxtuuIF33nmHO++8k+eeew6AmJgYAFpaOk9dT0pK4vzzz2fx4sW8+OKLDBkyhOOPPz5UXRSEgFAUhWXr9gFwzfQBmIMM2oiymHn0d2OJsZhZvvUgn/1SFo5mCk5E7FIZRtzV9DYlbjZYVT4jp/J5L+6M1ndBENpi6FQ+u3E9u3wju4wX3S4IWmbw4MGcffbZzJkzh2+//ZaNGzdy6aWXkpuby9lnnw3A//zP//Dpp5+yZ88e1q9fz4oVKxg+fDgA/fv3x2Qy8eGHH3Lo0CFqamo6vN4111zD999/z6JFi7j66qvD3j9BaM2aPUfYU15LQoyFM8f26dI5hmQnMeeEAQD87T+/SHXGMCJil8ow2iQXWlXlM9hE1zeSwbi7+Ub83AuC4IuhU/m8/m00oU9BIrsEQcu8+OKLTJw4kTPOOIOpU6eiKAofffQR0dGOzeyWlhZuvvlmhg8fzqmnnsqQIUN46qmnAMjNzeUvf/kLd999N9nZ2cydO7fDa02fPp2hQ4dSVVXF5ZdfHva+CUJr3vyvI6rrrHF9SLB2PTvpppmDyEmOZd+Rep7/Zneomie0QvLHVIYRzVnN3lX56mz0SoiJYGt6Fu9JvuEEH4nsEoQ2JMdGGU70d+GT2mzgVD6jCX1SjVEQ1M2SJUs6fD0tLY2XXnqp3dc78+e65557uOeeewJuz9atWwM+Vuh59h6u44Vvd/P1jnKO1NrISY5l5tBMLj8un9zUuEg3r1s0Nrfw+WZH2uF5E/p261wJ1ijm/XoYty3bwFMrd3HB5H5kJllD0UzBiy5Fdj355JPk5+cTGxvLlClTWLt2bbvHPvfccxx//PGkpaWRlpbGrFmz2hyvKAr33nsvvXv3Ji4ujlmzZrFjx46uNE3zGHGiZ+QIHyOn8hn5vgtCe3indRsNI3t2KT6eXcbqu/ezoLqhWcx6BUEQNMq/ftjPKQu/YumqIvaU11JZ38S2smqe+Xo3v3p0Jc9/s1vTY/z3Ow9T3dhMVpKVCf3Sun2+s8b2YWzfFOpsLTy10nherT1B0GLXG2+8wR133MF9993H+vXrGTt2LLNnz+bgwYN+j1+5ciUXXXQRK1asYNWqVeTl5XHKKadQXFzsPubvf/87//d//8eiRYtYs2YNCQkJzJ49m4aGhq73TKMYMYTfe8wz2o62eHY5MNrCVhDaI8XAYpdiYPHf9zlorKg2pZVVZ714lwiCIGiOV1YX8fu3NtLQZOfYgl68cMUkPr/9BJ68eAKT89NoaLLzt/9s4X/e2EBjszbH+Y83lQAwe2RO0Mb0/jCZTPx+9lAAXl29l+KK+m6fU/AlaLHrscceY86cOVx11VWMGDGCRYsWER8fz+LFi/0e/+qrr3LTTTcxbtw4hg0bxvPPP4/dbmf58uWAY7G/cOFC/vznP3P22WczZswYXnrpJQ4cOMC7777brc5pESOKXXYD72grBhZ8vFM4jZayJAjt4S12aXn3sysYO5XPuJGuCr6fc6P1XxAEQeus3n2Y+97fDMCc4wfw2rXH8qvh2QzOTuL0Mb158/qp/PXskUSZTby34QD/s2wDLXZtzXFa7ApfbHEE95w2Kidk550+KINjC3pha7HzxHJjZraFk6DELpvNxg8//MCsWbM8JzCbmTVrFqtWrQroHHV1dTQ1NdGrVy8A9uzZQ2lpqc85U1JSmDJlSrvnbGxspKqqyudLLxjRq0Ux8CTfyAsciewShLYkx3rErppGYz0P5FngwGhRba3XO0brvyAIgpapszVz55sbabEr/GZ8Ln/69fA2UU8mk4nLpubzwpWTibGY+XhTKfe8t0lTm3qbD1RypNZGojWKyQN6hey8JpOJu5zRXW/9sJ/C8tqQnVsIUuwqLy+npaWF7Oxsn59nZ2dTWloa0Dn++Mc/0qdPH7e45XpfMOecP38+KSkp7q+8vLxguqFqjLa4AZnkuzBadJORF7ZGIhiPR4CFCxcydOhQ4uLiyMvL4/bbbzdUSntMlOexbOTxsNJw46Hn343NdkOVIbe3WuwY7XMvGAstLe61jvyue4YnV+ykuKKe3NQ4/nbuKEym9tP7ZgzJ5P8uGofZBK+t2cuydft6sKXd45sd5QBMHZhOtKVLtuftMrF/L2YMyaTFrvDM11KZMZSE9k51wsMPP8yyZcv497//TWxsbJfPM2/ePCorK91f+/Zp5w8lEOwaC+vsLkb2KjF0ZJfdV+SUSYn+CNbj8bXXXuPuu+/mvvvuY8uWLbzwwgu88cYb/OlPf+rhlkcO778Co40JhvZvxLiCT+uhv9Jg8wDBGERHO6J26+rqItwS4+D6Xbt+90LoKams57mv9wBw75kjiI+J6vQ9p47q7fapuu/9zWwqrgxrG0PFt06x6/jBGWE5/9yTBgHw9g/7KasyziZvuOn8E+lFRkYGFouFsrIyn5+XlZWRk9Nx7uo//vEPHn74Yb744gvGjBnj/rnrfWVlZfTu3dvnnOPGjfN7LqvVitWqn9KcrRf5NbZmn1QWvWNkwQdDL+48NNsV6mwtJFiDGpIElePt8QiwaNEi/vOf/7B48WLuvvvuNsd///33TJs2jYsvvhiA/Px8LrroItasWdOj7Y4k3uPhUYNFNxk6yreVSXtlfRM5KV3fFNQSredAhpsHCIbAYrGQmprq3uyJj4/vMAJG6DqKolBXV8fBgwdJTU3FYrFEukm65flv9mBrsXNMfi9OGZHd+Ruc3HDCQNYXVfDFljLueHMDH9wyHWuUeu9Tna2Z/xYdAeD4wZlhucbk/F5Mzk9jXeFRnv9mN//v9BFhuY7RCGplGRMTw8SJE1m+fDnnnHMOgNtsfu7cue2+7+9//zsPPvggn376KZMmTfJ5bcCAAeTk5LB8+XK3uFVVVcWaNWu48cYbg+uNRmkdyFVZ12QosUtKrjswXt99/19R3yRil45weTzOmzfP/bPOPB6PO+44XnnlFdauXcsxxxzD7t27+eijj7jsssvavU5jYyONjY3u/2vdw9HIFVq9+17V0ESLXcESgmpHWqBtKp9xhE7x7BK8yc/Pp6ioyOdn8+fP99kgefPNN3nooYfYvn07mZmZzJ07l7vuuqvdc65cuZITTzzR72tr165l8uTJFBYWMmDAgDavr1q1imOPPbaLvfHFtcHfXnSzEFpSU1M7DcYQus7RWhuvr90LwE0nDgxKvDWbTTzy2zGc/PhXbC+r4Z9f7uTOU4aGq6nd5oeiozS1KOSmxpGfHh+269x04iCuenEdr67Zy00zB5GWEBO2axmFoFeWd9xxB1dccQWTJk3imGOOYeHChdTW1rp37i+//HJyc3OZP38+AAsWLODee+/ltddeIz8/3+3DlZiYSGJiIiaTif/5n//hb3/7G4MHD2bAgAHcc8899OnTxy2o6R1/fhV5ofO9Uz3evTdadFNrk3ZFUQyz09d6N7+izkZualyEWiOEmo48Hrdu3er3PRdffDHl5eVMnz4dRVFobm7mhhtu6DCNcf78+fzlL38JadsjiZFT+bzHQ0WB6oYmUuONMdHzJ/4bhTZzIAP1XfDPAw88wJw5c9z/T0pKcv/7448/5pJLLuGJJ57glFNOYcuWLcyZM4e4uLh2N96PO+44SkpKfH52zz33sHz58jab8F988QUjR450/z89PT0UXQIcRtS9e/cmKyuLpib5nIeT6OhoiegKM//6YT91thZG9E5mxpDgo53SEmJ44OxR3PTqep5auYtTR+Uwsk9KGFrafdYVHgXgmAG9wrpOmzkkkxG9k/mlpIol3xdy+8lDwnYtoxC02HXBBRdw6NAh7r33XkpLSxk3bhyffPKJe0Gzd+9ezGaPFdjTTz+NzWbjt7/9rc957rvvPu6//34A/vCHP1BbW8t1111HRUUF06dP55NPPumWr5eWaO1XIb5VxsG77812hVpbC4kGiW5qvcAx2r0X2rJy5UoeeughnnrqKaZMmcLOnTu57bbb+Otf/8o999zj9z3z5s3jjjvucP+/qqpK00VLfFL5ao31LPDnW2UUsYtWfTdSpG9rt0aJ7BKSkpLajch5+eWXOeecc7jhhhsAKCgoYN68eSxYsICbb77Z70I0JibG53xNTU2899573HLLLW2OT09PDzgaqKuRxRaLRYQYQdMoisKb/3V4Zl96bP8uC0C/Ht2bX4/O4aOfS7n//c28ef1UVW76/+BMYZzYPy2s1zGZTNx04kDmvvYjL68u4saZA4mNlrGiO3TJoH7u3LkUFRXR2NjImjVrmDJlivu1lStXsmTJEvf/CwsLURSlzZdL6ALHjX3ggQcoLS2loaGBL774giFDjKNkGr0SkZF9WtpO8o2zuG1jSmywe693uuLxeM8993DZZZdx7bXXMnr0aM4991weeugh5s+fj721qZETq9VKcnKyz5eWsUtklxsjCeCG7nurzlcZqO+Cfx5++GHS09MZP348jzzyCM3NnkrljY2NbTbD4+Li2L9/f5v0x/Z4//33OXz4sDsrxZuzzjqLrKwspk+fzvvvv9/hefRcHV4QOmLDvgp2HKwhNtrMGWN7d/6GDrjnjBHERVtYV3iUD38q6fwNPUxzi50f91YAMCk/vGIXwKkjc8hNjeNIrY33NxwI+/X0To9WYxT80zayy1gTPe81bFW9saryGdmY18iLOyPg7fHowuXxOHXqVL/vqaur84kMBty730YZF4zs2WXkdLa2fTfuxoeR+i605dZbb2XZsmWsWLGC66+/noceeog//OEP7tdnz57NO++8w/Lly7Hb7Wzfvp1HH30UoE2qYnu88MILzJ49m759+7p/lpiYyKOPPspbb73Ff/7zH6ZPn84555zToeCl9+rwgtAe76wvBuC0Ub277TPdOyWOG2cOBGD+R1uot7V0u32hZGtpNXW2FpKsUQzOSur8Dd0kymLmiuP6A7D4uz2Gmf+GC2PkS6mcNulcBoruaY2txU59U0tApWv1QJsKXAZa3Bp5YWsUgvV4PPPMM3nssccYP368O43xnnvu4cwzzzRMyoePZ5fRngVtjMqN0/82go+BngX+0lcFfXH33XezYMGCDo/ZsmULw4YN80lLHzNmDDExMVx//fXMnz8fq9XKnDlz2LVrF2eccQZNTU0kJydz2223cf/997fZLPHH/v37+fTTT3nzzTd9fp6RkeFz7cmTJ3PgwAEeeeQRzjrrLL/n0lt1eEEIBLtd4bNfHB7cZ43tE5JzXndCAW+s20dxRT3PfL2L/5mlngyvH4ocfl3j+6f1WNGcCyb1Y+EXO9haWs2qXYc5blBGj1xXjxhDUVA5Rver8JfGaRSxq80k38CCj9E+90YgWI/HP//5z5hMJv785z9TXFxMZmYmZ555Jg8++GCkutDj+KR1G2w8MLKPn5HFf6nGqH/uvPNOrrzyyg6PKSgo8PvzKVOm0NzcTGFhIUOHDsVkMrFgwQIeeughSktLyczMdEcQt3cOb1588UXS09PbFbBaX/vzzz/v9DhBMBIb91dQVtVIojWK4waFpoBDbLSFeb8extzXfuT5b/Zw+dR8eqmkEuFP+ysBGJeX2mPXTImP5rcT+/LSqiIWf7dHxK5uYAxFQeUYeZIL/hc4fQxSlc/IqXytfVqM1HcjMXfu3HYrZK1cudLn/1FRUdx3333cd999PdAydWI3cGSXkUUPI3sYtp4DiGeX/sjMzCQzM/hqbQAbNmzAbDaTlZXl83OLxUJubi4Ar7/+OlOnTu30Goqi8OKLL3L55ZcTHd156tWGDRvo3bt7fkSCoDc+3ezwYp05NBNrVOii7n89qjcj++xi84Eqnvl6F/NOGx6yc3eHzQccYtfo3J6tFHnlcfm8tKqI5VsPsqe8lgEZCT16fb0gnl0qQGmVymakCT60neQfNdACr3UetpH63nZha5y+C0J7eP9ZGE0ANnKxFmN7dvn2vbqxmaYW/wUpBH2zatUqFi5cyMaNG9m9ezevvvoqt99+O5deeilpaQ5j6PLychYtWsTWrVvZsGEDt912G2+99RYLFy50n2ft2rUMGzaM4uJin/N/+eWX7Nmzh2uvvbbNtZcuXcrrr7/O1q1b2bp1Kw899BCLFy/mlltuCWufBUFrfLHFIXadMjKwqqWBYjabuPMUR/ri0u8LOVjVENLzd4WGphZ2HKwBYFRuzxZAKshM5KRhWSiK4/chdA0Ru1RA61S2SgNNcsEjeiRZHYGGxtrRdnxPjjVi3x2dd/XdSEKfILRHa4N6IxmTuroabXF4YhhJAHf1PSXOEW1ytNY4zwLvvrvsUIwkdAoerFYry5YtY8aMGYwcOZIHH3yQ22+/nWeffdbnuKVLlzJp0iSmTZvG5s2bWblyJcccc4z79bq6OrZt20ZTk+/n6IUXXuC4445j2LBhfq//17/+lYkTJzJlyhTee+893njjDb8VGwXBqJRU1rPzYA1mE8wY3LVozY44cWgW4/ul0tBk56mVu0J+/mDZVlpNi10hPSGGnOTYzt8QYq6eNgCAt/67j5rG5k6OFvwhaYwqwMipG+BZ3KUnxlDd2MwRQy1wHH3PSLRS1dDMkVoD9d353dV3o33uBcEf3hE+zXaFmsZmkrpZ6UgruJ8FCVZKqxoMJYDb3X2PobK+yWB9d3yPMptIjY/hSK2No3U2MpPE+NtoTJgwgdWrV3d4TEZGBqtWrerwmJkzZ/rdKHjttdfafc8VV1zBFVdcEVhDBcGgfLfzMACj+6aSEh/6uYnJZOKuU4Zy8fNreG3NXq47oSCi1jabnCmMI/okYzL1jDm9N9MGpVOQmcDuQ7W8+2Mxlx7bv8fboHUksksFiGeXo/8uI8KjBhJ8XJN8d98NtMBRWt13Iwl9gtAerSu0GkkEbj0eHjFQ35VWfa+ztdDQpK7y6+HCNQcwmUykOhdP8jwQBEFQH9/uOATA8WE0TD9uUAZTBvTC1mLnuW92h+06gbCpuAqAUT3s1+XCZDJxyRSHwPXK6iJDRfuHChG7VEAbg3ajpa44v/dKcOziHjFQ+kYboc9Ai7vWfTdaypYg+KNNhVYDjgnpicbb+HDd95S4aHdpc6Pce9ewbzJBr3jj3XtBEAQtoCgK3zoju6aFuTrgzScOAmDZ2n0R3fz4xRnZNapPZMQugN9O6EtstJmtpdWs33s0Yu3QKiJ2qYFW63tbi506mzF2dMFTlS8j0SV6GGeS65rkpyc6hD4jTfDtrfpua7FTa6DPvSD4o01au4E8HF1dTzdwlK/ZbCIt3ljRri6R02yCNANu/AiCIGiBXYdqKK9pJDbazIT+qWG91vGDMxiVm0x9UwtLvtsT1mu1R1OLnS2l1UDPm9N7kxIfzZlj+gDwyuq9EWuHVhGxSwV4+1XEWBy3xFjpbI7vntQVA/Udj08LGKzvzvueEGMhNtr5uTfI4k4Q2qN1dKNRonvA27/RIYAbqSqfO5UP6JXgNKk3yPPA9ZE3m0yeyC6D9F0QBEErrC+qAGBM31SsUZawXstkMnHzTEd015LvCyNizl50uBZbs534GAt5afE9fn1vXF5d//mpxDAbYaFCxC4V4NnVNHn5VhlogeP8bkjPLuc6zpW2U1nfREvr0A6dorh9WnBHMsgCRzA63ildYKy/Cdd4mOpVlc8o/bd7CT6pho3sMpGaIJ5dgiAIasSVQjehX1qPXG/2yBwKMhOoamjm1dVFPXJNb3aU1QAwOCsRs7nnzem9GZuXyujcFGwtdt76776ItkVriNilAlzShskrhN9IET52r4qEYKz0hda+VYriELyMgPcCx2hpO4LQHp6qfC4PQ+P8TbgiXS0Wj+BjmI0f13ho9vhWGSWl33t7Rzy7BEEQ1IlH7ErtkeuZzSZumDEQgOe/3UNjc89anew86BC7BmUl9eh12+PSY/sB8NravW4LIKFzROxSAa4PrMnklb5goImekasxuoaqaIuZpNgowDiLW08Ei8mQ1SgFwR+u+UuGAU3avaOb0gxWlc/VdxMmz6aXQYQ+u5fQlybPAkEQBNVRWd/Edmek04T+PRPZBXDOuFxykmM5VN3IBxtLeuy6ADucYtfg7MQevW57nDm2D0mxURQdruObneWRbo5mELFLBSg+E3zjRbh4jModfa9ubMbWbAyfFsXLmNdTldAY997ula7lKjdvmCgOQWgHpVWk6xEDRrqaDZjabFf8bHoZpO+KvyhfA33uBUEQ1M6GfRUA9OsV756f9AQxUWYuP87hV/XCt3t6tGq7S+walKkOsSs+JorzJvQFiEhap1YRsUsFuFI3zEaNcHGOWylePi1GqUBm94puMq5PC8b83AuCH1p7GB6pbYxcY3oYxU90k1HGBLuBN718DOoNGN0uCIKgdtYX9WwKozcXH9OPuGgLW0qqWL37SI9cs8WusOuQuiK7AC6e4khl/HLrQQ5WN0S4NdpAxC4V4B3hYrRJLnhEjyiz2XA+LT4VuOJlN99In3tB8IfbsyvRWKls4Fu0wmjeTYqho9oc30149d0g910QBEELuCK7ejKF0UVqfAznTcwFHNFdPcG+I3XYmu1Yo8z0jXAlRm+GZCcxoV8qzXaFt38ojnRzNIGIXSrAt+S4sSa50Dp1xVg+LT4prO57b4zFracwg8efp8IgfReE9nBVJHSnMRoossvuZzw0ithnZA9D3xRO49kZCIIgqJ1fSqoAGNknJSLXv2raAACWby2jsLw27NdzpTAOzEzEEuFKjK25cLIjuuuNdXt7NK1Tq4jYpQLcO7pm7wm+MSa50Fr0MJZvleJtzGuwHW1vkdeIn3tB8IdrPExP8ES5GmUyY2TfKu++pyUYM8LZbDKRHGs8OwNBEAQ1c6i6kUPVjZhMMCwnMpUJB2YmcuLQTBQFlnxfGPbr7ThYDagrhdHF6WN6k2iNovBwXY+ldWoZEbtUgI9fhcHS+BRF8drR9hI9DLPAcXw3YfLy6DFW3w3rVScIflDcaYyOyC5bi51aW8+W244U3s9Co3kYuuRM73mAUfqO1xzAbDYZzs5AEAR9s6Wkihe+3cMjn27lxe/2sL2sOtJNCootzqiuAekJJFijItaOa6YXAPDmf/dRWR/e58NOlZnTe5NgjeLMsX0AR3SX0DGR+8QKbrz9KnoZTOzxDljwFfsM0n+8dvPjDZbG6F7YGs+jRhDawxXlkhBjIS7aQn1TC0dqbCRGcILZU3iKtXh5dhlkTPCNdHVEtdU3tdDQ1EJstCWCLQs/3hsf4LAzOFJrM47YJwiCLtl7uI4//ftnvt1Z3ua18f1S+dOvhzM5v1cEWhYcrhTG4b2TI9qOaYPSGZqdxLayat5ct485JxSE7VquVMkBmQlhu0Z3uHByHq+v3ctHm0q5v87m3iQS2iKRXSrAk77gFeFSazNE6ordq49mE6QmuDy7jCH4uPx5zF6+VUZZ3CntpO0Y4XMvCO3hXaHVaJsfrvHQZDJeNUbvqLZEaxRRzlw+I/Tfew4ExvQuFQRBX6wrPMLp//cN3+4sJ8ps4lfDsrh8an9mDs0kxmLmx70VnL9oFQs+2Upzi7r9CV2RXSP6RFbsMplMXDUtH4CXVxdht4dvvbD3SB0A+enqFLvG9E1heO9kbM123v1RjOo7QsQuFeAd4ZLqFDya7QpVDc0RbFXP4D1MmfBEdhnFs8vbqyQtwVhRbT4ir/O+GyllSxD84SsCu8R/Y5jU+zMqN0oqm2vSbjb7in1GiG7ypHA6vhsthVUQBH2xpaSKKxavpbqxmQn9Uvnyzpm8cOVkHjh7FEuuOoZv/3gi50/sC8DTK3dx82vraWxW79z3lwNOsSvCkV0AZ4/LJSk2ir1H6vhqx6GwXKOmsZnyGsfzp1+6eioxemMymbhwch4Ay9btk0CBDhCxSwV4T/Bjoy0kxDhSFowgenhHdpnMxvPs8vYrM9putnfqSlyMBWuUYzgywudeENrDx8MxwVWR0RiCjz/fqprGZlUvAkKFd6EWwFD+nd5zIMBwm16CIOiHqoYmbnzlB+psLUwtSOe1Oce2EUyykmN55PyxPHHReGIsZj7dXMZ1L/2gygq0DU0t7Drk8K+KdGQXQFyMhfMnOkSel1cVheUaRYcdKYy9EmJIjo0OyzVCwTnjcrFGmdlaWs3G/ZWRbo5qEbFLBSit/SoMJPi09uwyWkVCb88uV1RfRX0TLWEMzVUL3hGNYDyxTxD84Yn2hF6u1GajjIdefU+KjfJU5TOAj6G3Zxd4ovqMMB4qXhHO4F2dV//3XRAEffH3T7ZSeLiO3NQ4nrpkQoeei2eO7cOLV00mLtrCV9sPcfc7P6kuQmd7WTV2xTFHz0qyRro5AFw2tT8AK7YdZJ8z3TCUFB12nLNfL3VGdblIiY/m16N7A2JU3xEidqkAe6uJXi8DpbP5il3e5eaNMcn1jm5yCX2KAlVhrjKiBpRWu/lGM+gXBH94Inw8i/7DBngWgK9fmdnsGRONkM7W2qTdSOK/t1cbYDj/SkEQ9MHP+yt5dY1DdPjH+WPdz/COmDYog6cunYDFbOKd9cU8uWJnuJsZFNvLHFFdQ7OT3GN0pBmQkcDxgzNQFHhlTeiju1xiV75KUxi9ucCZyvj+hgPUNurf/qgriNilAlpr+Maa4HulMWK8yC5voTPaYibJWXHNCFF9rUVedySDQe69IPjD28su3UAbH+Avusk4go93VBsYy7fKLfA6vxvJr0wQBP0w/+MtKAqcPa4PUwemB/y+E4dm8dezRwHw2Ofb+X5X2+qNkcKVwjgoKzHCLfHlsmMd0V1vrttHQ1NorQ5caYz9VWpO782UAb0YkJFAra2FD386EOnmqBIRu1SAe9HvvBuG2tH1FrtMHqGvurFZlbnrocbbswswlEm9dxQHGEvkFYT28KnQarDIrtYp/UbyrVJajYe9DLTxY28l9IlnlyAIWmP93qN8v+swUWYTfzh1WNDvv3hKP86f2Be7Arct28DB6oYwtDJ4dh10iF0DM9Ul/PxqeDa5qXEcrWviPz+VhPTcrsiu/hqI7DKZTO7ormXr9kW4NepExC4V0MavIt44fhV2nzRGE8lx0R6flnr9T3Rb7+Z7Ihn0f+9b7+a7RF5Z4AiCy7PLOBsf0Hbjx12N0gD9b23SbqhnQTueXUYReQVB0D5POdMPzx2fS25qXJfO8cDZoxiancSh6kb+9M4mVfh37XRGdg1UWWSXxWzi4in9AHhpdWhTGbUU2QVw3oS+RJlN/Li3gm2l1ZFujuoQsUsFtNnNNlI6l9c4bjI5Bi8jpW+0jm5yGVIfqW2MVJN6jNa7+S6RVxY4gpHxpPKZDOXfCF7RTbTa+KnRf//benY5ngWHDfAsaDsHMtbnXhAEbbPvSB1fbDkIwA0zB3b5PHExFv7vovFEW0x8saWM9zdGNi2tqcXOXmeUk9rSGMHhVxVjMbNxXwU/7a8IyTkbmlooqXJE1WkhsgsgM8nKr4ZnAfCGRHe1QcQuFeAWPJz/d5ebN9BuNngmui6fGmMscHw9atITHffeCIKPezffqXZlJBpH5BSE9vCO8OllsAiX1tFN6e4xwQCCD77if7pzHnDYEM9B5z9a3fdaWwv1ttB6sQiCIISaN//rEBimD8pgYGb3RKGhOUncctJgAO57fzOHqiP3/Cs6XEezXSE+xkJOcmzE2tEeGYlWfj06B4CXV4Umumv/0ToUBRKtUe71qBZwpTL++8f9NDbLc9MbEbtUQOsJvpEiu3zFLsd31wKv3AD9b72j7ZrkG2GB08ajxkCLO0FoD58Krc6xsLK+ieYW43gYejY+HGOCkZ4FptbPAgP0vXWUb5I1ihiLY3pqhMg2QRC0S3OL3S12uQSH7nLjzIGM6J1MRV0Tf/lgc0jO2RVc5vQDMxNVU4mxNZdNzQfg/Y0HQrJu3nekHoC+aXGq7bM/ThicSU5yLEfrmvj8l7JIN0dViNilAtpUpXOlbhggsss7G901qGS4optq9D/Jbe1V4tpFMELf20a1uURO/fddENrDLfiYITUu2r0JYgTvptbPQo/4r/8xwW733fRyPQeP1Nrcr+mV1vfdZDIZauNHEATt8v2uw5RVNZIWH80pI7NDcs5oi5m//3YMZhN8+FMJq3YdDsl5g2XnQXVWYvRmQr9URvROprHZztvr93f7fMUVHrFLS0RZzJw/qS8gqYytEbFLDbSqyNfLQGW3W0e1gbGim+yt7r07dcUQ997x3WzyTWM0wn0XhPZQvDy7oixmUuKckb5G2PxoNR56Nj703/fW46Fr06vFrlBZr3+hEzx9B+/INv0LnYIgaJePNzkqAZ42ujfWKEvIzjsqN8VtwP6XDzZHJLrbE9mlXqN2k8nEpcf2B+DVNXu7bep/wCl2dbXIQCQ5f6IjsvDbneXsO1IX4daoBxG7VEBbY1pXVTr9p660TlsBb8FH/5Pc9jxqyg2wuGtdidJ13yvrm7A16/tzLwjt0Tqlq5eBCna08a0yUCqfq++uJ2FMlEfo1Puz0O+mlyuF1QDPQkEQtElzi53PNjtSxn49qnfIz//7U4aSFh/N1tJqXglxxcFA2HXIUZVQzZFdAGeN60OiNYo95bXdjoJzRXb10aDY1S89nmmD0lEUeOuH7ke56QURu1SAZ6LnmOmlxse4J/t6T2VsvbADgwk+zu9mQ6ZwOr67FjgpcdFYnB8EI0SxCII/3F7drTY/jBTdZGrl2XW0zmbMjR+DPAvtzltr8hfZpfO+C4KgXdYWHuFwrY20+GimFPQK+flT42P4/eyhADz2+fYe3/QqLHeIXfkZ6o3sAoeZ/Dnj+wCO6K7u4I7s0lgao4sLJjuiAf/133206NwCIVBE7FIBrQoRYTGb3Gbd5dX6nui1NuUF73Q2Iwg+/j1qjOTT4rr3ZrPJU5zAAPdeEPzRnneTEf4m2vpXOjzLFEX/nmX+Nn4yDFK0w7Pp4/mZkTZ+BEHQJl/8chCAWcOzibaEZ0l94eR+jOyTTFVDM//8cmdYruGPyromdwp9v17xPXbdrnLxMY5Uxk83l3KwuqHL5yk+qt3ILoBTRmSTEhfNgcoGvtlxKNLNUQUidqkA9yTX625kGMSvorVJOUB6ovF8q1r7tTXbFaoa9L64c3z3TWGV3XzB2LSO8MlIMo747x4Pnf+Pspjd3lV6fxb62/gxim+V33lAgnFSWAVB0CYuMeHEYVlhu4bFbGLeacMBeHl1YY95Me11XiczyUp8TFSPXLM7jOiTzIR+qTTbFd76b9dS+Jpb7JRWOYSyvhoVu2KjLZw7PhcQo3oXInapgNbRPeCdvqDvSa5/zy5ndJMBBI/Wu/nWKAtJsY6Hiu5TV/xFMiQax69NEPzR+u/Clcp3SOfjAeB+IHhv/BhFAO+oWIvenwX+50DGiWgUBEF7lFY2sONgDWYTHDcwPazXmj44g+MHZ9DUovDoZ9vCei0XLrFLC1FdLi6Z4ojuem3N3i6l8JVWNWBXIMZidq9HtMgFkx1G9V9sKZNnKCJ2qQL/qXwGSV9wi12en7kmudWNzTQ0tUSgVT1HR/feCIbU4Lu4M5I/kSD4o42PX5Jx0rlae3aBcTZ+/Ee6GuPed3Tf5VkgCIIacUV1je6bSqozAjmc/PHUYQC8u+EAm4orw369oiMOvy4tiV2nj+lNSlw0xRX1fL09+BS+AxWOqK7eqbGYvRemGmN472TG9k2hqUXh3+uLI92ciCNilwponboBxqlE1Nq3CSA5Nopoi+P/ehd8WnvUgHckg94XOB1FNOr7vgtCe7j+LlxkGkTsAf/pbMbZ+PEX6WoMwcffpleGgaoyC4KgPb7ZUQ7ACYMzeuR6o3JTOGusw4R9wSdbw369fRqM7IqNtvDbiX0BeHVN8NUriyscfe6Tos0URm9cRvVv/Hefe35hVETsUgEdVyTU90TPX+qGyWTy2tE2xiTf17PMee/1LvT5qcAlpsSC0XFH+JhdRSuMsfEB/qObjJLa7N+zyxh97yiF83CNzfATdUEQ1IXdrvDdTofYdfzgzB677u9PGUq0xcQ3O8pZu+dIWK9VdFh7YhfAxVMcIs+XWw9S7KysGCiuyC6tVmL05syxvYmLtrDzYA3r9x6NdHMiiohdKsCfX0WmQRb9/hY34C346Lv/HXmV6P3eK4gpsSC0oVWUi5EEYL/joUFSm/0KPgbpu7/77lOspb45Iu0SBEHwx65DNRyutREXbWF8v9Qeu26/9HjOn+TwY1r4xfawXsvt2ZWuLbFrYGYiUwvSsSvwxtq9Qb13v8YrMXqTFBvN6WN6A7BsrbGN6kXsUgGeHV3PzzxVmPQ9yXWt7FqnRqcbJHWldTVGgAyDLHD8etQYaGEvCP7wpPI5Pbucz4JaWwv1NmN4GPrzcNR7ZFtH46HeI7xb+9SBIx0lyeos1qLzTS9BELTFD0WOSJmxeSlEW3p2KX3ziYOItpj4ftdh1uw+HJZr2JrtHHBGRfXXWGQXwCXHOqK7lq3bR1OLPeD3ufqs1UqMrXEZ1X/4UwnVDU0Rbk3kELFLBfg3Z3VOcqv1Pcnz13cwoG+Vue2Ott5TV/x51Ihnl2B0Wqe1J1qjiIlyPKr1LnrY/eR1ezZ+9N33jjy7qhqasTUHPmHXGnZ726g2EJN6QRDUiUvsmtg/rcevnZsax+/c0V07wnKNAxX12BWwRpnJTNJeVcJTRuSQkRjDwepGlm8pC/h9JZUOsat3amy4mtajTOqfRkFmAvVNLXz4U0mkmxMxROxSAf48uzK8fJv07Ffhr+9gnHQ2/55dxopk8PHsSjBWJUpBaE3rvwuTyeROa9e72OUvwscwJu3O7yavp0FybDRRZv0Xa2l308tdmVjfn3vBl/z8fEwmk8/Xww8/7HPMm2++ybhx44iPj6d///488sgjnZ53+/btnH322WRkZJCcnMz06dNZsWKFzzF79+7l9NNPJz4+nqysLO666y6amyWNVvDlh72RE7sAbnJGd63afZjVYYjuKvIyp289LmuBmCizWxB8dU3gqYyllQ7PrpxkfYhdJpOJC53RXW+sM24qo4hdKsDfJNdl0G5rtlPdqN8HrT9TXjBS+kb7FQn1HtWmdGBKXN/UQp1Nv597QfCH98aG380PnQs+/qvTGiO12Z9nl9lsckf66vlZ2Nmml94/90JbHnjgAUpKStxft9xyi/u1jz/+mEsuuYQbbriBTZs28dRTT/H444/zz3/+s8NznnHGGTQ3N/Pll1/yww8/MHbsWM444wxKS0sBaGlp4fTTT8dms/H999+zdOlSlixZwr333hvWvgra4kitjd2HagEYnxcZsSs3Nc6dovb456H37tqrwUqMrbnomH6YTI6qmYXltZ0eX29roarBse7ITtGH2AXwmwl9iTKb2LCvgq2lVZFuTkQQsUsFuNMXvO5GXIyFhBgLoO8d7XYnuQbZzff4tHh+lpFojOgmfx418TEWrM6ULb3fe0FojXcQr9+qfDoWPKA9zy5jeJa1X6zFGBs/0H7f5VlgPJKSksjJyXF/JSQkuF97+eWXOeecc7jhhhsoKCjg9NNPZ968eSxYsKDdTIjy8nJ27NjB3XffzZgxYxg8eDAPP/wwdXV1bNq0CYDPPvuMX375hVdeeYVx48Zx2mmn8de//pUnn3wSm00+g4KDH51RXQMzE0hzCvKR4KaZg4ixmFmz5wjf7yoP6bnd3lUarkqY1yuemUMclTJfD8CovqzKEdUV5+UXqQcyEq3MGp4NGDe6S8QuFeBvNxsgI0n/CxxPGl+rvjsXOPoXfPx41DgfnkfrmmgOwlhRa/jzqDGZTG6xzwiLO0Hwxt5pZJe+/yb8RTd5e5bp2bfLX6QrGKMio6cogy8ZBvFrE9ry8MMPk56ezvjx43nkkUd8UgkbGxuJjfWNvIiLi2P//v0UFRX5PV96ejpDhw7lpZdeora2lubmZp555hmysrKYOHEiAKtWrWL06NFkZ2e73zd79myqqqrYvHmz3/M2NjZSVVXl8yXom0j6dXnTxyu668kVO0N6bpfYpfWqhJdM6Q/Am//dR2Nzx5tlLrErO9mqydTNjrjgGMfn5N8/Fnf6e9AjInapALtTz2jPpF3PCxx/O/lgnNQVxc9ufmp8jPv3caROvwscd/quQaP6BKE13jEJPl52BvHx85fWbjKZDFGh1t+zAIxh0N95oRr93nehLbfeeivLli1jxYoVXH/99Tz00EP84Q9/cL8+e/Zs3nnnHZYvX47dbmf79u08+uijAJSU+DdhNplMfPHFF/z4448kJSURGxvLY489xieffEJamkO0KC0t9RG6APf/XamOrZk/fz4pKSnur7y8vG73X1A3PxdXAjAuQimM3lw/o4Aos4nvdh5mw76KkJ1XL2LXicOy6JMSy9G6Jj7Z5P9v2EWZsyBctk78urw5YXAmvVNiqahr4rPNgRv26wURu1SAx7PLFyMYlXt28v1P8PVs0O/rz+Ppv8XLp0XPk/x2771BqlEKQmvai+wySipbe1HO7nQ2HY8J7ftW6T+Vr91NL4N87o3A3Xff3cZ0vvXX1q1bAbjjjjuYOXMmY8aM4YYbbuDRRx/liSeeoLHR8TmYM2cOc+fO5YwzziAmJoZjjz2WCy+8EACz2f+yRlEUbr75ZrKysvjmm29Yu3Yt55xzDmeeeWa7AlkgzJs3j8rKSvfXvn3GTBMyCoqisPmAI3pvVG5yhFsDfdPiOWtcHwCeCmF014EKR5RTH41XJbSYTVx4TD8AXl3dcSpjWaUrskvbffaHxWzi/Il9AUeUm9EQsUsFtDfJzTCAX4W/tBXw9F3PBv12Lw2vvQWOnif5rojG9j1q9Pu5FwR/tOfZZZQ0xvZFD2f/q/U7JrQn/mckOfp+SMf3vl0rB+ezQM99Nwp33nknW7Zs6fCroKDA73unTJlCc3MzhYWFgONvZMGCBdTU1FBUVERpaSnHHHMMQLvn+PLLL/nwww9ZtmwZ06ZNY8KECTz11FPExcWxdOlSAHJycigr8416cP0/JyfH73mtVivJyck+X4J+Ka1q4EitDYvZxJDspEg3B4CbZg7EZILPfiljR1l1t8/XYlcorXKJXdqO7AK4YHIeFrOJtYVH2N7B78c7jVGPnO+sTvnNjnL2OQsQGAURu1SA0u5ET/8LnPZMeWO9DAIPVeuz/95RHK09yzKdfm167Tu0L/Iaoe+C4I/2IrsyDSIAe7ybWo2HBhA9PCmcvj93913H42F7fmXyLNAPmZmZDBs2rMOvmBj/Zt8bNmzAbDaTlZXl83OLxUJubi4xMTG8/vrrTJ06lczMTL/nqKtzLO5aR36ZzWbszp23qVOn8vPPP3Pw4EH3659//jnJycmMGDGiy30X9MPmYkdU16DMRGKjLRFujYNBWUnMHuEQY59euavb5ztU3UiLXcFiNpGVpP0op+zkWE52GrS/tqb96C49pzGCw7B/+qAMAN4yWHRXl8SuJ598kvz8fGJjY5kyZQpr165t99jNmzdz3nnnkZ+fj8lkYuHChW2Ouf/++9uEMw8bNqwrTdMk7U1yjZHO5V/wAP1PdH2iOFr9Jeq97960u7A1QN8FwRvFJ9rTuNUY2xM9Djp3XvVIexs/Wc6Jt57Hw/Y8u1z3vbqhmYYm45nqGpFVq1axcOFCNm7cyO7du3n11Ve5/fbbufTSS93eWuXl5SxatIitW7eyYcMGbrvtNt566y2f9cXatWsZNmwYxcXFgEPISktL44orrmDjxo1s376du+66iz179nD66acDcMoppzBixAguu+wyNm7cyKeffsqf//xnbr75ZqxWfUZ7CMHhSmEc2UddEXw3nTgQgPc2Huh21E6x068rJzkWi7/FmQa55FhHKuPb6/dTZ/OfLaTnNEYXroIGb/2wnxa7Pi2C/BG02PXGG29wxx13cN9997F+/XrGjh3L7NmzfXZCvKmrq6OgoICHH3643TBggJEjR1JSUuL++vbbb4NtmmZpb6Lnqsao59389ib44LXA0ekk396OZxdAls77Du1HdmUlu/qu34WtIPjD3o4/oSvK92hdE006rtDaXjqbazzUd2RXO5GuBhD/20tfTY6NwuqsxKnn/gserFYry5YtY8aMGYwcOZIHH3yQ22+/nWeffdbnuKVLlzJp0iSmTZvG5s2bWblypTuVERxrj23bttHU1ARARkYGn3zyCTU1NZx00klMmjSJb7/9lvfee4+xY8cCjkixDz/8EIvFwtSpU7n00ku5/PLLeeCBB3ruFyComl9KHOb0I1Qmdo3pm8rxgzNosSs8+/Xubp3LY06vH9Fn2sAM+qfHU93QzIcb/Xv0lVXrX+w6ZWQ2qfHRlFQ28PWOQ5FuTo8RFewbHnvsMebMmcNVV10FwKJFi/jPf/7D4sWLufvuu9scP3nyZCZPngzg93V3Q6KiOhTDvGlsbHQbVQKaL/XbmTFtuY4neXa30tf2Nb1HNykdeHbpve/QwW6+ARZ3guAPezuRXWnOCq12BY7U2nQ7GbO3E9nlim46WKXfMaHdFM4klzm/jaYWO9EW/blPtOfdaTKZyEyysv9oPQerG8nrFR+B1gk9yYQJE1i9enWHx2RkZLBq1aoOj5k5c2ab4kaTJk3i008/7fB9/fv356OPPgqssYLh8ER2pUS4JW25ceZAvtlRzhv/3cctvxrU5RTEkkqH2NU7Rft+XS7MZhMXH9OP+R9v5dU1Rfxusm/VVEVRKHVGduXodH4FYI2ycO74XF78rpA31+3jxKFZnb9JBwQ1a7LZbPzwww/MmjXLcwKzmVmzZnX64OmMHTt20KdPHwoKCrjkkkvYu7f9vFq9lfpVOpnk6nnR75qKdBTZpdf+i2eX/wWOEdJ2jEQwae8AFRUV3HzzzfTu3Rur1cqQIUOMs/hoRwA3m030StD/mOBJa29nPNRzZJfze+vxsFdCjDuVRK/FatrzLQWvqD6J9BUEIYJU1jWx/6hDCFJbZBfA1IJ0xvdLxdZsZ/G3hV0+j6cSo37ELoDfTuxLjMXMxv2V/Ly/0ue1qvpmGpsdUfNZOjWod+FKZfz8lzJde4J7E5TYVV5eTktLC9nZ2T4/z87OprS0tMuNmDJlCkuWLOGTTz7h6aefZs+ePRx//PFUV/uvmqC3Ur9uwafV3XD9wVU3NlNv06dfRXtRbYB7V0Kv6Wzee47tetTotO+A+xfQ3sK21tZCrU4rcRqFYNPebTYbJ598MoWFhfzrX/9i27ZtPPfcc+Tm5vZwyyNDIKnNeha77O2ks7nTuqsa20Rr6IX2UvotZpPbv1Ov997Im16CIGiDraWOqK7c1DhS4qIj3Jq2mEwmbpo5CIBXVhdRWd/UpfO4PLtydZTGCA7v09NGOzLIXltb5POaK4UxJS5aNYUHwsWwnGTG5qXSbFd4Z/3+SDenR1BFPPxpp53G+eefz5gxY5g9ezYfffQRFRUVvPnmm36P11upX1cqX+t0riRrFLHRjlukV9HDbUjsJ49R75NcWdj6FzoTYizEOR82eu6/EfBOex8xYgSLFi0iPj6exYsX+z1+8eLFHDlyhHfffZdp06aRn5/PjBkz3J4q/mhsbKSqqsrnS6v4RHsa0MvOE+3pXwCvb2qhVqcbP+7oJj+zMr3f+/aifEH/3p2CIGiDHQdrABiSnRjhlrTPr4ZlMTQ7iZrGZl5ZXdT5G/ygxzRGF5dM6Q/AexsOUNXgEQONkMLozYXO6K431u3T7QaiN0GJXRkZGVgsFsrKynx+XlZWFrDfViCkpqYyZMgQdu7cGbJzqpn2dnRNJpNXdJM+J3qBTHL1KngoXj7TbSO7HPe9SsdVqNrz7DKZTF6LO33eeyPQlbT3999/n6lTp3LzzTeTnZ3NqFGjeOihh2hpaf9vQE9p7d6eXa3/Lryjm/SKZ+PH9+fxMVEkWh0Wo3qtyNjhxo/OfQztHfTdNQfSa98FQdAGO51i1+DspAi3pH3MZhM3znRUZnzxuz1dWj/oNY0RYHJ+GoOzEqmztfDej8Xun5c55xV6T2F0ccaY3sRFW9h1qJYfio5GujlhJyixKyYmhokTJ7J8+XL3z+x2O8uXL2fq1Kkha1RNTQ27du2id+/eITunmnF7dfh5Te8LHKUdoQ/0H92k0H5kV3JsFDE6r0Ll6r9foVPnizsj0JW09927d/Ovf/2LlpYWPvroI+655x4effRR/va3v7V7HT2ltSsEktat378JI6ezdbTxo/d735GdgUR2CYKgBlxi16BM9UZ2AZw+pje5qXGU19h4O8g0tYamFo7UOrwh9VSN0YXJZOKSKf0AeHXNXndUk0vsMkpkV1JsNGeMcWgsb6zT7pw5UIJOY7zjjjt47rnnWLp0KVu2bOHGG2+ktrbWXZ3x8ssvZ968ee7jbTYbGzZsYMOGDdhsNoqLi9mwYYNP1Nbvf/97vvrqKwoLC/n+++8599xzsVgsXHTRRSHoovppr+Q4GCd9wV/qhmuSe6TOUYVKb/hWXvN9zRHVp29TZrvzlvoVOpPFlNiI2O12srKyePbZZ5k4cSIXXHAB/+///T8WLVrU7nv0lNautBPtCPp/FoD35kfb1/Quetg7MGnXu9Dn9m/0c+P1vuklCII22HHQ4SM9SMVpjADRFjPXTB8AwHNf76bFHniammucjYkyq9KXLBScO6EvsdFmtpZWs36vI6qp1CV2pRhD7AK48BhHFsSHP5VQ3dA1fzetELTYdcEFF/CPf/yDe++9l3HjxrFhwwY++eQT9+793r17KSkpcR9/4MABxo8fz/jx4ykpKeEf//gH48eP59prr3Ufs3//fi666CKGDh3K7373O9LT01m9ejWZmZkh6KL66WiSq/cd3Y5SN3rFO6pQKYo+q1D5+vO0v8DRb1RfB7v5ifpe2BqBrqS99+7dmyFDhmCxeAxChw8fTmlpKTab/saA1gRWsEO/fxNGFnw8ad1tX9O70GlkOwNBENRPVUMTZc65+KAsdYtd4Ki4lxIXTeHhOj7bHHgBOVeEU3ay1e+6RA+kxEVz1tg+ALy6ei8ApZWOe5ttkMgugAn90hiUlUh9UwvvbTgQ6eaElS4Z1M+dO5eioiIaGxtZs2YNU6ZMcb+2cuVKlixZ4v5/fn4+iqK0+Vq5cqX7mGXLlnHgwAEaGxvZv38/y5YtY+DAgV3ulNawd5DHqHvBo4O0HbPZREaifqtQdTTBB69UPr1Gdhm4OIER6Era+7Rp09i5cyd2uyeSc/v27fTu3ZuYmJiwtznSBBTZpdNnAfhuALQmS+eRXe1VpwX9p3UH4tlVXtPo9nQTBEHoSVwpjNnJVpJj1R/xlGCN4oqpDjP2RV/tCtiE3CXoZSfpW/RxGdV/+HMJR2ttlFY5TPmNksYIjnnmRce0TenUI6qoxmh0AvGt0u2OrnNN294OQqae+9/BfQevVD69GjJ34NllhCgWIxBs2vuNN97IkSNHuO2229i+fTv/+c9/eOihh7j55psj1YUepePILo/goddJidJBOpveBfAO773OC3Z0lL6a7tzwarYrHK3Tf3SnIAjqw+3XpYGoLheXH5ePNcrMxv2VrNlzJKD3uNZaeo9wGtM3hVG5ydia7by9fr87sstIaYwA503IxRplZktJFev3VkS6OWFDxC4V0PEkV9+ViDrqO+i7EpO9gwk+QGais+86j+wyYsqSUQg27T0vL49PP/2UdevWMWbMGG699VZuu+027r777kh1oUfpsCKf82/C1mKnok6f/godiR4eAVyf4r8n0tdfZJfnOahHobOj9NVoi5leCQ7BS69inyAI6sZdiTFLvZUYW5ORaOX8SX0BeOarXQG9xxXZ5Zpv6BWHUb0juuvF7woprzFeGiNAanwMZ7pSOtcURbg14UPELhXgmrz6D+HX946uvYO0HdB3+kZHixvwNmnXX9/By7Org+IEehX6jEQwae8AU6dOZfXq1TQ0NLBr1y7+9Kc/+Xh46ZmOxB5rlIXUeEf6hH6fB+0/C/UugHtS+dri6ntjs53qxuaea1QPoXSS0i8m9YIgRBKX2DVQQ5FdANdOL8BsghXbDrG1tKrT4w9WGSOyC+CssX1ItEZRXOFIYUyyRrmtc4yEqzrlhz85Ujr1iIhdKsCTutH2Ndck70itDVuz/ioS0oFnF+i7ApdnYecfvZu0d+zT4uj74ZrGoCrJCIKW6SjCBfSf1u76S+8sjVOPePretvNxMRaSrFGAPj3bOt300vE8QBAE9bP7kFPsykyIcEuCIz8jgdNG9Qbg2a92d3q8a4zNTtZ3ZBc4fM3OHZ/r/v+QnCTdmvJ3xLi8VEb28aR06hERu1RARxO9tPgYopwz/3IdRrl0NsnVc3RTR15toO++Q8fVGNMTrZhNjs/H4Vp99l8QWmNXOgjvwSuVT4eCB3Qc7ere+Kmz0dSiv42fjiJdATJ1/DzoqFAN6D+qTxAE9dLcYmf/UUf0T366tsQugOtOKADg/Y0H3FFM7VFmoMgugIudUU0Axw1Mj2BLIod3Suera/bqshCMiF0qoCPfKrPZpOtdzcCjm/QXydBRyhJ4Jvh6rULVkdBpMZvolaD/6nOC4E1HPnbgEcDLdDkeKh2Oia6NH0WBwzX6C7XvKIUT9P0s7Oxzr+tCNYIgqJoDFQ002xViosyarNY3Ni+VqQXpNNsVFn+7p8NjXWJXls49u1wM753MTTMHctqoHK6dXhDp5kSMs8c5Ujr3lNeyavfhSDcn5IjYpQJcMka7k1zXRE+HVfkCjm7SZVRbx55d6U6xp6lFoaJef4bU9k58WsS3SzAegRXs0KMA7O277m9MNJtNZOjYw9Ed1NfevddxsRrF4Cn9giCol8LDtQD07xXvt1KwFrhh5kAAXl+7l8p2Ctw0NLVQ1eDwhMzSoKjXVf5w6jCevnQiKU5PVCPindL5ymr9GdWL2KUCOkrnAn2b1HcqeCR6Fnd6q0LlETn9ExPlXYVKf0InHWdseTx6dLiwFwR/dBrZpeN0Lu/RvV3Rw9n/Mh1u/HSa0q/jeUBnQp8rpUaeBYIg9DRFR+oA6J8eH+GWdJ0TBmcwLCeJOlsLL68u9HuMaxMtNtpMcmxUD7ZOUAOXHOtI6fzslzLdzbFE7FIBnUX4ZLpLrutvohdoZFdjs51KnUU3dSb0gWeSX1qpr4EHOjfjdoWLl+ps0BWE9uhsTHCNh3oUv+1emxntjQnZOh4TOtv0co2HJTp+FrQ3B8pJcfa9qmO/GUEQhFBTVO6M7NKgX5cLk8nEDTMc0V1Lvi+koamlzTGueUVWUqwhjdqNzrCcZCb1T6PFrvDGun2Rbk5IEbFLBdg7S19w7+brb5LrMqZtr++x0RbSnKGlelvgeKpwtv9QyUnWbyRDR9XHALJT9Lu4EwR/KJ1G9+h/4wPA1M7MJCdFx+NhJxs/rvGwTIfjYWd9dwl9ZTqM8BYEQd0UHtZ+ZBfA6WN6k5saR3mNzW/VvbIq41RiFPzjiu56fe1emnVUCEjELhUQaHSTHn1a7M6/pY52EbJ1uqPdmU8JeO1o66zv0HkUi2eBo7++C4I/OipWAp6Nj7KqBt0t+r0ju9obE3unxAH6jnRtv+/6jWrrdMPPOQeyNds52o7fjCAIQjjYe0T7kV0A0RYz10wfAMBzX++mpVXhK7c5vYH8ugRfThvVm7T4aEoqG1ix7VCkmxMyROxSAZ2lL2Q7d/N1WYHL+b0jz8ccne5odxbZBB6hT4+CT2c+La4oDj0ubAXBH4FufDQ02alubO6pZvU4hkxjdP2jE/G/VIdCp9JJYQZrlIV0p3+lPA8EQegp7HaFImdkV77GI7sALpicR0pcNIWH6/hsc6nPa66IcdeaUzAesdEWzp+UB+jLqF7ELhUQqF9FaaUOI7sCiG7S6452IJ5dOTr27OosZSsn2RHFoUehTxD80dl4GB8T5TaO1Zv47xPZ1Zngo7O+g/emV8dCp63ZToXOopvcz4IOZgJ63vgRBEGdHKxupLHZjsVsok9qXKSb020SrFFcPrU/AIu+2uWzcXLQHdklaYxG5uJjHKmMX+84RKHTr07riNilAjwTPf+4xJ7ymkZszfrJoQU8FfkMGN3UmdgDHp+WUh2msHa2m+8SeQ/X2mhsbmumKQh6I5AxwZXKp7fUZu9gpXa9m1zRnjp7FkDn8wBrlMVdnVd/9z6AjR+dbnoJgqBeCg87Fvt90+KItuhjyXzFcflYo8xs3F/J6t1H3D93ja05ksZoaPIzEpg5NBNFgZdW6SO6Sx9/uRqns5LjvRJiiHEOsroTfDoRPEC/u/nBRLXp7b6D1+e+nd9AWnw0MVGOz70e/eoEoTUBRXum6Hs87Igcp9BX3dBMrc7SON1p7R08DPW68dPZHAj0690pCIJ62etMYezXS/spjC4yEq2cP6kvAM98vcv98zKJ7BKcXHFcPgBv/XefLuZaInapgM5MiU0mk253Ne2dGZWg3+imzjyrwCP0Ham1+S0VrGU686ozmUzuqjB6+9wLgj9c42FHPn69dVq0wlvqaq//idYoEq2ONE69jQlGTul33fuANn509rkXBEG97K+oB6Bvmn7ELoBrpxdgNsHKbYfYUlIFeFdjlMguozNjcCYDMhKobmzmHT+VO7WGiF0qwojeTR5D5vaP8fS9vgda1HN0ZkYNkBIXjVWn0U32AFY4ev3cC4J/Aoh0dQseOhsPvTL0O3oWugRwvYkeAaW163Q87GzjA3wN+gVBEHqC4qMusUv7fl3e5GckcNqo3oCjMmNNYzM1zggeEbsEs9nEFU5vtyXfF2q+KI6IXSqgM2Na0H/qSkeLG9eO7tG6Jl1FN7lSODvazdZzVJ+LjkyJXWlLekvbEQR/BJLOpd/ILs9kKqBnoc7GhGAKluhtPAzGv1JvfRcEQb0UVzjSGHN1YE7fmutOKADg/Y0H2LC3AvCNnhaMzXkT+5IQY2HXoVq+3Vke6eZ0CxG7VIDHu6h99LvAcWDE6KZAJvjgtZuvo0m+9y5Bx7v5zjRGnX3uBcEfnZmUg0cA1tvfhN1r47DD/ifr26C/Y6HPMR7qru/BeHfq6DkoCIK6OVDhGG/0UImxNWPzUplakE6zXeGBDzcDns0kQUiKjeb8SXkALPmuMLKN6SYidqkAd4RPQLvZOktdCWA3W6/RTYHs5IPXbr6OFjg+C9tA0nZ0dN8FoT2CiXTVneDhJYB3bNDvTGPU2ZigBODZpddIV7srhbWjOZDzWVChswhvQRDUid2uUOK0T8nVWRqjixtmDgRge1kNAAMzEyLZHEFlXO5MZfxy20GKnJVJtYiIXSrAk7rS/jF69S4ydHST83tHO/mgz7SdgCO7JHVFMBCBjIeuv4nK+ibqbNqvkuMiUAFct89C5/cOI7t0+ByEwCK7kuOiiIu2APq794IgqI+D1Y00tShYzCayk/RZofCEwRkMy0ly/39YTnIEWyOojYLMRGYMyURR4KVVRZFuTpcRsUsFBJa+oM8JfiAVqECfJvWBRLWBPhd3vilLnS/u9BbFIgj+CGTRn2SNIiFGf4t+T4Rzx8dl69S3KhjPLr1FN3msHNrvvF4jvAVBUCcuv66c5FiiLPpcLptMJm6YMdD9/5lDMyPYGkGNXDktH4A31+2jtlGbG6z6/OvVGIGkL/R2pS9UN9LirRRonECEPvAquV6pP88uQ0Z2eZlRmzoYhVx9P1jVqPlqIILQGUqwi34diV0E/CzQt2dXR91PjosiNtoxYOrp3gdSlRm8KnHq6FkoCII62X9U3ymMLs4Y05vbfjWYh84dzfh+aZFujqAyZgzOZEBGAtWNzbzzY3Gkm9MlROxSAYFMcjOTrFjMJlrsCuU1+hF8AvWt0uNufiCFCUCf5eYVn8iu9slKcvTd1mLnSK0tvI0ShAgTyLMA9Cn4BDweOj27ymsaaW6xd3K0dnANiZ0Jna57r6vNDwNHOQuCoE6KK5xilw7N6b2Jspi5/eQhXDylX6SbIqgQs9nEFU7vriXf7dFk4IGIXSogEIN6i9lEVpI+KzFBAGmMeoxuCnSC74puqm7ArpOoPu+xsqNIjpgoMxmJMYC+7r0g+MMj/hs32rOzyK6MBCtRZhN2BQ7paOMn0OeBHqOb3EJfZ96dOi3OIAiC+jhgELFLEDrjvIl9SbRGsetQLSu3H4p0c4JGxC4VEIhBPeg7wifQVL6SCv14dnnue8d9z0qyYjJBU4tCea0+Fnf2ACuvgfe918/nXhD84TEp7/g4T0VG/Y2Hne18mM0m97PwgI7GhECfhX2ckV166nug3p2uRaeePveCIKiTYoOkMQpCZyTFRnPB5DwAnv9md4RbEzwidqmA4H2r9DPRsyuB5a64Jrll1fpJXXFH9HVyXLTFTHaSvgQf7/i0gBd3OvrcC4I/Ak3r1qNnlyuyqTOhD6BPqkvs0s+YEOi975PqErv003cjC32CIKgTo6QxCkIgXDUtH4vZxHc7D7P5QGWkmxMUInapgEAM6sErwkWH6QudTXIzE61EWxyeZWXV+ohuCnSCD57FXbFOFjjB5Hy7Fnd66bsgtEuQGx96SucKxJzfRa4eBR/n987EPj2Oh0YW+gRBUCcuUb2PiF2CQN+0eH49ujcAz3+zJ8KtCQ4Ru1RAoIJPbx2mcwWavmA2e4x59TLRDdSMGiA3LR7QT9+9rcc663/fNNnNF4xBoONhTrLTpFyHYldgkV06FHwCzOPMTdPXc9Cbzp4FLpHzcK2NeltLD7RIEAQjUt3QRE1jM+BZewmC0Zlz/AAAPth4QFN2AiJ2qQAj72p2JbpJL/0PpDCBC71FdnnnMXaauqLDz70g+MMd8Njp34RjPDhca6OhSR+L/kDN+UGfgk+gkV25ensWEPg8IDkuikRrFCBp7YIghI+yKkcGSZI1igTnmCMIRmdM31SOGdCLZrvCku8LI92cgBGxSwV4Inw62dHV4W52oBWoQH+7+W6D+gCO1Vvajo9BfSfHuu/7UX30XRDawx6gb1VKXDQJMRZAP+NhgP70gPezQH+RbZ3NA1x9r25opqqhKdzN6hHsAaa1m0wm3W16CW3Jz8/HZDL5fD388MM+x7z55puMGzeO+Ph4+vfvzyOPPNLpebdv387ZZ59NRkYGycnJTJ8+nRUrVvgc0/q6JpOJZcuWhbR/gvpxVbvNlqguQfDhuuMLAHhtzV539KPaEbFLBQRcici5m11W1UCTXkzaA5zgA/TVmejhNmQO4K9Qb0JnMAb1nuIE+vncC4I/AhV8TCaT+3mgl/Ew0Ahn8BoPj9aFs0k9SqAG/fExUaTFRwP6EXyCi/DW18aP4J8HHniAkpIS99ctt9zifu3jjz/mkksu4YYbbmDTpk089dRTPP744/zzn//s8JxnnHEGzc3NfPnll/zwww+MHTuWM844g9LSUp/jXnzxRZ9rn3POOeHooqBiXBYBOckidgmCNycNy6IgM4HqhmbeWLcv0s0JCBG7VECAmStkJFiJiTJjV/Tj1WIPwrdKb5PcYAyZPX3Xy333iuzqpPvpCTHERJlRdPS5FwR/BLPo150AHsTGh2s8rGpoplo30U2O78E9D/Rx77smdOqj74J/kpKSyMnJcX8lJCS4X3v55Zc555xzuOGGGygoKOD0009n3rx5LFiwoN3iN+Xl5ezYsYO7776bMWPGMHjwYB5++GHq6urYtGmTz7Gpqak+146NFcHDaJS6IrtE7BIEH8xmE9dMd3h3Lf52D80aCEIQsUsNBLjAMZtN9HGG1O7XyUTP5VsVjCmxXgSfrvT9iE6Meb3no50tbr0/93pZ3AmCP4JJ63ZFdu3XSXRToJFNAInWKFLiHNFNeqlI6fFw7PxYvaVxBlqkB/TXd8E/Dz/8MOnp6YwfP55HHnmE5mZPukxjY2MbASouLo79+/dTVFTk93zp6ekMHTqUl156idraWpqbm3nmmWfIyspi4sSJPsfefPPNZGRkcMwxx7B48eIOq0c3NjZSVVXl8yUEzuGaRp7/ZjeXPL+aqfOXM+lvn3PK419x+xsb+Pjnkoh5UrrSGHNSrBG5viComfMm9KVXQgzFFfV8srm08zdEGHHdUwFB7WqmxVF4uE43u/ld2c0urqhHUZSAIgDUjN0lhgfQj+RYhzFvTWMzxRX1DMpKDG/jwkwwi3pw3Hs9fe4FwR+eKN8A0rqdFVr1EuESTN/BMSZU1jdRfLSeIdlJ4WtYDxFUdV6dRTcpAVo5gP78K4W23HrrrUyYMIFevXrx/fffM2/ePEpKSnjssccAmD17NrfffjtXXnklJ554Ijt37uTRRx8FoKSkhPz8/DbnNJlMfPHFF5xzzjkkJSVhNpvJysrik08+IS0tzX3cAw88wEknnUR8fDyfffYZN910EzU1Ndx6661+2zp//nz+8pe/hP6XoHPsdoXnvtnN/y3fQW2rDdzyGhvby2r494/FZCRauWFGAZdN7Y81ytJj7XNlEUhklyC0JTbawmXH9ud/l+/g2a93c/ro3qpek4vYpQKCSd/Q2ySXLqQv1DQ2U9XQ7N7Z1yqBVt8Cp0dPahzbyqo5oAexy/k9kJ18kAWOYAwC9W8E/aYxBjIegqP/W0qqdNf/YOYBehkPu5LCqpf7bhTuvvtuFixY0OExW7ZsYdiwYdxxxx3un40ZM4aYmBiuv/565s+fj9VqZc6cOezatYszzjiDpqYmkpOTue2227j//vsxt2OCqigKN998M1lZWXzzzTfExcXx/PPPc+aZZ7Ju3Tp69+4NwD333ON+z/jx46mtreWRRx5pV+yaN2+eT3urqqrIy8sL+PdiRGoam7nltfWs2HYIgBG9k/ndpL6MyUslPsZCSUUDq3Yf5oONByipbOBv/9nC62v3suC8MUzK79UjbSyrdlRjFLFLEPxz+dT+PPP1Ln7aX8l3Ow8zfXBGpJvULpLGqAKCW+A4d/MrdJK64vweiOgRF2OhV0IMoI9JfjD3HdBVFarg+y6pK4L+CcqzS6cG9YGOCrk6Gg+hqyn9+uh7sNHtACWV9djt7aeXCerizjvvZMuWLR1+FRQU+H3vlClTaG5uprCwEHCIogsWLKCmpoaioiJKS0s55phjANo9x5dffsmHH37IsmXLmDZtGhMmTOCpp54iLi6OpUuXttvuKVOmsH//fhobG/2+brVaSU5O9vkS2qfO1szVL65jxbZDWKPMPPyb0Xx4y3SunDaACf3SGJaTzInDsvjTr4fz9R9OZMF5o8lMsrLrUC2/e2YVT6/c1WFaaagoE4N6QeiQ9EQrF07uB8CTK3ZGuDUdI5FdKiAYwce9wNHZJDdQ+qTGcqTWxoGKeob31vakIpjdbNDXAieYRT3oL4pFEPwRzKLfVZ221FmdN9qi7b2roCO7dPcsdHwPLKXfsQDTS9+DmQNlJ1kxm6CpRaG8ppEsWYxqgszMTDIzM7v03g0bNrjTDr2xWCzk5uYC8PrrrzN16tR2r1FX59ggbh35ZTabsdvbN1jesGEDaWlpWK3i3dRdFEXhjjc2srbwCEnWKF6+dgrj8lLbPT7aYuaCyf04dWRv/vLBZt75sZgFn2zlp/0VPH7BOGKjw5PW2GJXOFTjEDdzUmR8EYT2uO6EAl5ZXcSq3Yf5oegoE/undf6mCKDt2bFOCMqUWG8m7QYWPYIxZAYvQ2o99N31jyD7rgehTxA6I5DxMCPRSoxFP9V5gxH6QF/iPwT3PHCNh2VOoVPreIS+zomymN3RFnqYBwi+rFq1ioULF7Jx40Z2797Nq6++yu23386ll17q9tYqLy9n0aJFbN26lQ0bNnDbbbfx1ltvsXDhQvd51q5dy7BhwyguLgZg6tSppKWlccUVV7Bx40a2b9/OXXfdxZ49ezj99NMB+OCDD3j++efZtGkTO3fu5Omnn+ahhx7illtu6fHfgx5Z/F0hn2wuJdpiYvFVkzsUurxJiY/m0d+NZf5vRhNjMfPxplKuWLyWqjBV4i2vaaTFrmA2OZ6zgiD4p09qHL+Z4NhweHqleqO7ROxSAcHsaPf12s3WQwi/e5Ib5AJHD5NcT9KO8XyrXJ/dQIU+74VtT4SwC0IkCEbwMZtN7ggfvVTnhcA3PvrozL/SHkQWZ0aCR+h0VQ3TNEEKnXqL6hM8WK1Wli1bxowZMxg5ciQPPvggt99+O88++6zPcUuXLmXSpElMmzaNzZs3s3LlSncqIzgiubZt20ZTk0MQycjI4JNPPqGmpoaTTjqJSZMm8e233/Lee+8xduxYAKKjo3nyySeZOnUq48aN45lnnuGxxx7jvvvu67lfgE7ZfaiGBZ9sBeCeM0YwOUjvLZPJxEXH9OOla44hyRrFmj1HuPCZ1RyttYW8ra7No8wkK5ZAJ6mCYFBumDEQkwm+2HKQraXqrEYraYwqwONf1PmgmpMSi9kEtmY75bWNZCVpO8Q2GJ8S0FdkW9cjGbTfdxeBCn29naHkdbYWKuubSI2PCWezBCEiBJva3DctXjdVSoP18fNO42xusROl4TRObwE/ELHPbDbROzWWosN1FB+td1fm1CrBFixxPAuP6kboFDxMmDCB1atXd3hMRkYGq1at6vCYmTNnttkYmzRpEp9++mm77zn11FM59dRTA2+sEBCKovDndzdha7ZzwpBMLju2f5fPdWxBOsuuP5YrFq/jl5IqrnhxLa9cO4Xk2NAVrCqtEr8uQQiUgsxEfj26N//5qYSnV+7ify8cH+kmtUG7s0MdEUxKV7TF7K4OooeJXrCLO5fYtf+o9g36gyk1D76RXS0aj+oLVuiLjba4w8n3HdH+514Q/BFMOhfoqzpvsM8C7zTOEo2ncXqvyQMW+1xp7Tq4927vzgA7r6e+C4Le+WLLQb7fdRhrlJm/nT0q4DG+PUb2SeH1OVPolRDDT/sruWbJOupszSFqLRx0il1SiVEQAuOmmQMB+GDjAYoO10a4NW0RsUsFiG9V4KJHXi/HDrYeBI9gd7Ozk2OJsZhptivunSetEuxnHqBfL8fnfp8OhE5B8Eew46EnnUv7fxPBCuBms8ktemh9TPDeugh0TOznfBbuPaLtvkPwzwM99V0Q9IzdrvDoZ9sAuHr6APqlhyYKdXB2Ei9dfQxJsVGsKzzKTa+upzlE/oXuyC4xpxeEgBjZJ4UTh2ZiV2DRV7sj3Zw2iNilAoJN39BTyflgKlCBR+wqr2kM6U5OJAh2YWsxm9z3fu9hbU/yg/3Mg7fQqe2+C0J7GHrjw/k9mE1/vYwJ3ulWgfbflbqo9b5D8BGNea6+a1zkFAS98/GmUraWVpMUG8X1JxSE9NyjclNYctUxxEabWbntEH/54JeQeLoeqnZUYsxKEnN6QQiUm08cBMDbP+xXnZeoiF0qwhzg3dDLbjYEX24+JS6alDhHbr7Wo7u6Et2km8Wd83swC1vZzRf0jsvD0IipbJ5qhMFEe+pjTPDOSg80xcfVd33MA4K7967n4P4j+ijUIwh6ZfF3ewC4atqAsHitTuyfxsILxmMywcuri1j8XWG3z1le4zC9l0qMghA4k/J7ccyAXtha7DyjsuguEbtUQDAG9eA9wdf+AifY1BXQ0wIn+Em6K5VP6333RLUFL/Rpve+C0B7B+la5/iaKj9aHLIUjUgRp2wTo51moEHxkl16egxC8f2XvlFiizCZsLXbKqtW1gywIgoOf9lfwQ9FRoi0mLj22X9iuc+qoHP502nAA/vafX/jil7Junc8V2ZUpkV2CEBS3nOSI7np1TZHb+04NiNilAoKd6OklusebQIU+gDyXd5PG+x/swhb0k74RbEQfeEUyaPy+C0J72IN8FuR4+fhp3aTd3o1IV60LPt77HsFGN5VVNdLQ1BKOZvUY7ojGAG99lMXsrk6s9QhvQdArS5xRVmeM6RP2yvHXHj+Ai6f0Q1Hg9jc2sKe86ybZ5TUOsUsiuwQhOKYPymBi/zQam+08tXJXpJvjRsQuFWAPMsqlf3oC4KhIqJeqfMGIHnpZ4HSl73rZzfekMQa/sC3WQTVKQfCHa9Ef6JhgNpvoq7Noz2BCu1wbH/s133fPvwPtflp8NInWKED7aazBeneCfp6FgqBHKuua+PCnEgCuOC4/7NczmUzcf+ZIJvVPo7qxmetf/i+1jcH7+iqK4hG7JLJLEILCZDJx+6whALy2di+lKtmEFbFLBQQb5ZKTHEu0xURTi0JJpbYnuYpH9Qj4PXqJ8HF3PYj36CWqrytCn/fnXuvVKAXBH11Z9Pd3jglFGi9aEWx1WvCMh4drbdR0YWGjFuxdMKg3mUy6eR4oBt70EgQ98tGmEmwtdoblJDEuL7VHrhkTZeapSyaQmWRle1kNf3z7p6AN6yvrm2hqcbwnIzH0HmOCoHemDUrnmPxe2JrtPL1yZ6SbA4jYpQqUIBc4FrPJnc6m9YlesJEMoJ8d3S4ZMqe7qlHaurRrpRY884/A+24xm9wVyLRejVIQ/OIaE4J4MrsifYuOdD1tQw10pUJrcmw0afGugiXaHRO8l2NBiX1peonqc3wPLtJXH1F9gqBH3v2xGICzx+X26HWzkmN5+pIJRJlNfPhTCS98uyeo97uiupJjo7BGWcLRREHQNSaTif85eTAAr6/dp4qgHBG7VECwfhXgtaup8UV/VyIZvH2rQlFmOFIE69UGjsWdqxqlllNXuhLZBV6RbRr3LBMEf3RpPNRJdA/uCOfgBgU9bH50pVgJ6KPvINV5BUFPFFfUs2bPEQDOHtenx68/Kb8Xfz7dYVg//+Ot/FB0NOD3HhRzekHoNscNzGCKszLjUysi790lYpcK6IpZd/90fUz0utL3PqlxmE3Q0GTnkHMXRovYu7CbDfqY5HdF6ANPJIPmF/aC4AdPldLA36OXNMZgzfld6EHs64pBPXgifbXcdzC2f6Ug6I0PNh4AYMqAXu5CEj3NFcflc+bYPrTYFW59/Ucq65oCel95jQ0Qc3pB6C63n+zw7npj3T6KKyIbnNElsevJJ58kPz+f2NhYpkyZwtq1a9s9dvPmzZx33nnk5+djMplYuHBht8+pNzy7usH7VhVpfKLXlcVdTJSZ3inaFz3cEX1Bvk8Pk/xgU3dd6KHvgtAeXRHA3RsfhzUe6UrX2q4PsSt4zy5AP3YGXUhrdz0LDlZrvxqlIOiJz38pA+CMsT0f1eXCZDLx0Lmj6NcrnuKKeu5+JzD/rkMS2SUIIeHYgnSmFqRja7Hz5IrIencFLXa98cYb3HHHHdx3332sX7+esWPHMnv2bA4ePOj3+Lq6OgoKCnj44YfJyckJyTn1hseYN/D36MakXaKbgk7b0cXirgtebaCP+y4I7dGdohXVjc1UBLh7rUbsBk5j7Gpkl/ezQA9CZzDPg5S4aJLc1Si1e+8FQU8crmlk/V5H2uCs4VkRbUtSbDT/vHg80RYTH28q5ZU1ezt9j7sSo0R2CUK3cUV3vfXffRFdswYtdj322GPMmTOHq666ihEjRrBo0SLi4+NZvHix3+MnT57MI488woUXXojV6n/wCPaceqMru/mu9AXtp64EH9kFHnPafUe061vVlag28O67du99V1M4PYs77d53o9HVqN1ly5ZhMpk455xzwttAFdGVqnSx0Raykx3PVi1H+nZ1PNSF2OX172C639eZ1l1ra+GoloVOu+N7MM8D72qUWr73gqAnVmw7hKLAiN7J7gyMSDKmbyp/PHUYAH/98Be2lFR1eLxEdglC6DhmQC+mD8qgqUXh8S+2R6wdQYldNpuNH374gVmzZnlOYDYza9YsVq1a1aUGdOWcjY2NVFVV+Xxpmm74VVTWNwWci65GulJuHrzSODUs9nW77xqe4Hdd6HNVo2zUdDVKo9DVqN3CwkJ+//vfc/zxx/dQS9VBVyNd+/dyVmQ8rN2KjF2NdHVHOR+tx27XZnSTvYtpjLHRFnKSYwEo1PC9d9HVSN/Ccu0+CwVBT3y51ZHCGOmoLm+umT6Ak4ZlYWu2M/e19dTZ2p87uiK7MiWySxBCwl2zhwLw7x+L2VoaGb0mKLGrvLyclpYWsrOzfX6enZ1NaWlplxrQlXPOnz+flJQU91deXl6Xrq0WumLMGx8T5Q6z1fKuZlfKzQP0T3cs7rQ8wbd7Sq8FRb6z73sP19Gi2cWd43uwYldKXDS9EmIAbd97o9CVqN2WlhYuueQS/vKXv1BQUNCDrY08XanMC/owKu9q3/ukxhFtMWFrtnNABSWuu4J3BmKwQmd+hkvw0e546JkHBNt37c8DBEEv2JrtfL29HIBfDc/u5Oiew2Qy8Y/zx5KdbGXXoVr++uEv7R4rkV2CEFrG5qVy+ujeKAo88sm2iLRBk9UY582bR2Vlpftr3759kW5St/BM8oPczddDRcYu7uYPcE1yNTzB74o/DzgWdzEWM7YWOwciXOGi67iiGYPtPeSny26+FuhqJPADDzxAVlYW11xzTUDX0VOkr0f/Nl6ka1dS2QAsZpPmI3y6kr7qYkBGIgB7tPws7OLmR4FzHqDlvguCXli/9yg1jc1kJMYwOjcl0s3xoVdCDI9fMA6TCV5fu49PN/sPphDPLkEIPXeeMgSL2cTyrQdZu+dIj18/KLErIyMDi8VCWVmZz8/LysraNZ8PxzmtVivJyck+X1pG6VqAj7vkvJZ3Nbvq2eUSuw7X2qis12YaZ1cNmS1mk1vo1Ookv6ufefBe3NWErkFCyOlK1O63337LCy+8wHPPPRfwdfQU6etJ5Qvuff114OHYVfEftD8muPveBfF/QIa2nwXQ9XlAvohdgqAaVu06DMDUgRmYu6Lch5njBmZw3fGOaPG73/6Jg1UNPq/b7QqHa2yARHYJQigpyEzkgsmOufnDH2/p8YI6QYldMTExTJw4keXLl7t/ZrfbWb58OVOnTu1SA8JxTq3RVbPuATqY6HX1455gjSLL+TDSanRXV32rQPvpG10V+sCzuNut0fsu+Ke6uprLLruM5557joyMjIDfp6dI3y4v+p2pzXs0Oh5Ad6ObtD0mdDWdH3QS2eX83tUI7+KKehqbW0LcKkEQgmHVbqfYVZAe4Za0zx2nDGFE72SO1jVx179+8ll0V9Q30eycnKYnxkSqiYKgS2771WBio82s31vB57+Udf6GEBJ0GuMdd9zBc889x9KlS9myZQs33ngjtbW1XHXVVQBcfvnlzJs3z328zWZjw4YNbNiwAZvNRnFxMRs2bGDnzp0Bn1PvdHWSPyBTB2JXt0QPbfe/O313pW/sPqTVvnc9tMu1uNOqyGkUgo3a3bVrF4WFhZx55plERUURFRXFSy+9xPvvv09UVBS7du3yex29RfpCFxb9zmfBoepGqhu0HenategmbY8J3XsOejy7enq3NFR0deMnIzGGRGsUiuLwsBQEITI0NLWwYW8FAFMHqlfsskZZ+N8Lx2GNMvPV9kO8tKrI/ZrLFiQjMYZoiyZdfgRBtWQnx3L1tAEA/P3TbTS32Hvs2kH/NV9wwQX84x//4N5772XcuHFs2LCBTz75xJ2qsnfvXkpKStzHHzhwgPHjxzN+/HhKSkr4xz/+wfjx47n22msDPqfeUbro0zLALXhoM3UDuh7JAF791+oCh67v5ms9faM7kV35OkjbMQLBRu0OGzaMn3/+2b05smHDBs466yxOPPFENmzYoOn0xEBxFa0I9s8iOTba7TGi3b+L7kR2aX087Lr4n9crHrMJam0tbnNlrdFVsc9kMmn+3guCHvih6Ci2Fjs5ybFuX1W1Mjg7iT/9ejgAD320hR1l1QDsP+oQu3LT1N1+QdAq188YSGp8NDsP1vDO+uIeu25UV940d+5c5s6d6/e1lStX+vw/Pz8/oN3Gjs6pd5QuTvJdk7yjdU0crbWRlqC9sNuupi+A9k3quxfJoO00xm4Jfemez31FnY3UeO197o3CHXfcwRVXXMGkSZM45phjWLhwYZtI4NzcXObPn09sbCyjRo3yeX9qaipAm5/rle54NxVkJlBe08juQ7WM6Zsa0nb1BF015wfPeLjvaD1NLXbN7cp31asNHJEKfdPi2Xukjj3ltWQlx4a2cT1Ad9I48zMS+Lm4UsQuQYggHr+u9C49v3qay6f258utB/lq+yFuXbaBd28+jmJnZFfftLgIt04Q9ElKXDQ3zxzEgx9t4fEvtnPm2D7ExVjCfl1tzQh1it29wgnuffExUfROcUxsterVEgrfKq1OcrtagQq8FndH6rA191woaKjoTt8TrFHkOBd0Wr33RiHYSGCj051F/8BMbUf6Kl18DgJkJ1uJi7bQYlfYp8HqxF2N7nah+Weh83t3noVa3fgRBD3gqrB2bEGvCLckMEwmE4+cP4ZeCTFsKanisc+2s/+o49nRN1XELkEIF5dN7U9uahwllQ288O3uHrmmiF0qwOPZ1bXdfNCyd5Pje5ciGbwiu7ToVdLViD6ArCQr8TEW7ArsO6rdxV1XPvMgqYxaYu7cuRQVFdHY2MiaNWuYMmWK+7WVK1eyZMmSdt+7ZMkS3n333fA3UiWEQgDXalq3vRsG9VpPZ+vOswA8z0Ltbno5vnenGqVW50CCoHWaWuz8VFwBwMT+2hC7ALKSYnn4N6MBePab3fzrv/sBiewShHASG23hD6cOBeDplbs4WN3QyTu6j4hdKqAbdh1eE3xt7uZ3J5KhX3o8JhNUNzZT7iwXrCW6s5vvs7jT4CTf3k1xUuuG1ILgj25tfDj/JrS66PcEdnVN8dG02NUNsQdwe+Ro8VkAoalGKZFdghAZtpVW09BkJzk2yi28a4VTRuZw0TF5KIpjLQFQkJkY4VYJgr45c0wfxvZNodbWwuOf7wj79UTsUgFu36oubOtqvex4dyJ8rFEWcp3hxlrsf3dSOEHbqSvd8WoDr918DfZdENqjG5l8PtV57XYNRrq6hL4uzkq0LHZ1p1ALwIBMjQs+3alG6fRwLKtqpNa5WBUEoef4ce9RAMb1S+vSOibS3HPGCLclDMC4vNTINUYQDIDZbOLPZ4wA4K3/7qO0MrzRXSJ2qQClG7uaWk9jtHcjbQe0bVLfHYN60HbqSrcXd7KbL+gQz99F8H8Y/XrFE2U2Ud/UQlkPhIWHGiP7VnVH5ASvlP7DdbRoUOjszvMgJT6aXs7iPPI8EISe58e9FQCM16hIFB8TxbOXTaIgI4F7zhhBgrVLtdsEQQiCyfm9uOPkIbxz03HkpIS3sI6IXSqgO4JPQYa2d/O7U24ePP3fpcE0zu7484CnKqEmDam76dnlncKpRb82QfBHd8aEaIuZfr2061/UfQFcuxs/nqi2rnW+T2ocMVFmbM12io/Wh7JpPUL3I32d8wAN3ntB0Do/7qsAYFy/1Ii2ozuM7pvCl7+fyTXTB0S6KYJgGG791eAeqR4uYpcKcJnTdmU3v29aPNEWE43NdkqqtLeb351y8wADsxwRPrsOak/w6Y4hM8AgZ993HtTeBN/zme/a+/unO6JYam0tlIQ5/FUQeopQLfq1mN7bXd8q13hYWtVAdUNTqJrVI3THtxPAYja5N352HqoOTaN6kO56OA52Pwu1Nw8QBC1ztNbmjqYd1wOLVkEQhGARsUsF2O2O712Z6FrMJvprOMJH6eYs37XA2aHBSW53DZldQl95TSNHa7Vl0O/+zHdxYRttMbvTlrR47wXBH90x6gbvtHbt/U10V/xPiYsmK8kKaE/0sHcz0hW8noVl2uo7eHl3dvHmezZ+tCf0CYKW2bi/AnBstKQ504kFQRDUhIhdKqKrE92BzgWO1ib40P1IhsFZSQDsPVJHQ1NLiFrVMyjdXNwlWqPcBv07Nba47a5HDXh283eUyQJH0AndXPS7qkhp+VnQrTEhW5ubH92NdAXPs1CT976bkW1aFvoEQctsPlAFwJi+KRFuiSAIgn9E7FIB3fUqcU1yt2twomfv5iQ3IzGG1PhoFAV2aU3w6WbaDmh3kt/dKA7wiF1au++C0B7djewarNHxAOi2jx9oV/BxRbp2R+rTqtAHHrGvy5te2Y77vqe8lqYWeydHC4IQKn4pcYhdI3onR7glgiAI/hGxSwUo3Zzkuye5Goxw6W65eZPJpFm/ju6KnKBdr5KQCH3OBY4mF/aC4Ifu/l24Fv2lVQ1Uacy3KhTj4SCNRnsq3SzUAt6pfDWaK9rR3arMfVJiSYix0GxXKJKKjILQY2xxRnaN6CNilyAI6kTELhXQXXPWIdmuyK5qzU1yu1tuHmCQRnfzu+vZBd67+Rpb3IUwsmuHBhd3guCP7i76U+KiyU52+FZpTQT2/AV3J7LLKfhoLNqzu5V5wVGd12I2UdPYTFlVY2ga1kMo3YxoNJlMPmKfIAjhp7axmT1OcXm4RHYJgqBSROxSAd31rSrITMBsgqqGZg5Wa2ySGwKvEiOn8ml1gh8KoW9AhuNzX1nfxKEabX3uBcEf7vGwG+cY4o541JYAHpLUZmff9x+tp87WHIpm9Qjdje4GiIkyk58eD2hw88P5vTuRvgM1Og8QBK2ytbQaRYHsZCsZidZIN0cQBMEvInapgO7u6lqjLOQ7KzJqbaLX3ap84B3ho60JPiHYzR+U6VjclVQ2UK2htCV7dx2JgdhoC/16ORZ3OzX2uRcEf4RC9NCqh2Moopt6JcSQnhCDosDuQ9pJZwuFyAmee6+1eUAo7r277xrb+BEErfLLgUpA/LoEQVA3InapACUU3k3OdLbtGtvND4VXiavvhYfrsDVrx5w2FLvZKfHRZCU5dtS0FN3lWdR37zzuFFaNpS0Jgj9C8SwYovnU5u4NCgM1uPnhSV/tXt+1aFLvnYLePZFXe30XBC3jNqcXvy5BEFSMiF0qoLtpjOCVuqKhCT54V2Pset9zkmNJtEbRYlco1JA5rd3e/YUtaHSB4/zenfsO3sUZtNN3QWiPUIgeg708HLWER/zv3nm0WJEyFCIneNLad2noWWD3aF3dehq4ngW7DtXQ4n1SQRDCwi8uc/reKRFuiSAIQvuI2KUCultuHrwXONqZ5AJe5ea7fgpvc1pNLXCc37st+GRpz6Onu1U4XbgWtlpb2AuCP0KRzuZa9JdVNVJZr6HUZrf4393x0DUmaOdZYA9B+ip4xK7tB7VTrCZUkV190+KJiTJja7az70hdKJomRIj8/HxMJpPP18MPP+xzzJtvvsm4ceOIj4+nf//+PPLII52ed/369Zx88smkpqaSnp7OddddR02N7zixd+9eTj/9dOLj48nKyuKuu+6iuVk7/n89hd2uuMfYYb2TItwaQRCE9hGxSwV0t9w8+C76tTLJhdCUmwdP6s620qruNqnHCIUhM3insGpncReKKpyg7UqkgtCaUHh2JcdG0zslFtCYAO783l3fqiGajGwLzXNwYGYiZhNU1DVppliNTxBWN/pvMZvc86CtpVq694I/HnjgAUpKStxft9xyi/u1jz/+mEsuuYQbbriBTZs28dRTT/H444/zz3/+s93zHThwgFmzZjFo0CDWrFnDJ598wubNm7nyyivdx7S0tHD66adjs9n4/vvvWbp0KUuWLOHee+8NZ1c1SXFFPfVNLcRYzPR3eqcKgiCoERG7VEAoBJ+CTEfZ8WqNVWQMRbl5gGE5Ds+ALRqa5IbClBe8+l6iPaEvFGk7ZhMc1dDiThDawx6iMUGLkb6him4a5jRL3nukTjNFO0LV99hoCwWZDsFHK88DBe/Iru6da3hv7T0LBf8kJSWRk5Pj/kpISHC/9vLLL3POOedwww03UFBQwOmnn868efNYsGBBu5teH374IdHR0Tz55JMMHTqUyZMns2jRIt5++2127twJwGeffcYvv/zCK6+8wrhx4zjttNP461//ypNPPonNZuuRfmsFl0dsQWYCURZZSgqCoF5khFIDIZjoOioyOnZXtLSrGaroJlcYtZYmuaEyZB6W4+j7wepGDtdoQ/AJRTQj+C7uftHQvRcE/4RmPByiwfTeUPlW9UqIITvZUbRDK/0PQXFaNx7BR1t9h+4/D1zPwq0aivAW/PPwww+Tnp7O+PHjeeSRR3xSCRsbG4mNjfU5Pi4ujv3791NUVOT3fI2NjcTExGD28k6Ii4sD4NtvvwVg1apVjB49muzsbPcxs2fPpqqqis2bN7d73qqqKp8vI+DyB3alTguCIKgVEbtUQCg8u0Cbu5qhEj1cpY/3H62nSiO7+aFK20mwRtFfY0JnqERO0ObnXhD8YXcWk+3ueOj6m9CSAByKFE4Xnv5razwMhdrlEny0Mh56i13dfR6M0JjQJ/jn1ltvZdmyZaxYsYLrr7+ehx56iD/84Q/u12fPns0777zD8uXLsdvtbN++nUcffRSAkpISv+c86aSTKC0t5ZFHHsFms3H06FHuvvtun/eUlpb6CF2A+/+lpaV+zzt//nxSUlLcX3l5ed3rvEZw+eO6PGMFQRDUiohdKiAU1RjBa4J/QBuTXPDue/fOkxof4/ap2aYxwae7C1uA4RpLZQyV0Acw3BnVt1UWOILGUULk3eQqBb/lQJVmvOxCYc7vwvUs3KqV8TCEQp9L8NFKdJNd8TYzCF0Ka02jmIqribvvvruN6Xzrr61btwJwxx13MHPmTMaMGcMNN9zAo48+yhNPPEFjoyNyfc6cOcydO5czzjiDmJgYjj32WC688EIAn8gtb0aOHMnSpUt59NFHiY+PJycnhwEDBpCdnd3uewJh3rx5VFZWur/27dvX5XNpCVf1b5dnrCAIgloRsUsFhMq7ybXA0dZufmgWd+CVwqCR/ofqvoMWU1dCk8IJ2hP6BKE93J5d3Vz0D8xMJMZiprqxmf1H60PQsvCjhFAB11x0UwiFPldK/65DtTQ0tYTgjOHFx58+hCmsWtn0Mgp33nknW7Zs6fCroKDA73unTJlCc3MzhYWFgGODcMGCBdTU1FBUVERpaSnHHHMMQLvnALj44ospLS2luLiYw4cPc//993Po0CH3e3JycigrK/N5j+v/OTk5fs9ptVpJTk72+dI7iqK4PbskjVEQBLUTFekGCL47m91hpFPw2H2ohoamFmKjLSE5bzgJVRojOASfFdsOaSh1xfE9FIKP1jzLwiH07S6v1cznXhD8Eaq/i5goM4OzE9l8oIrNB6rI00C1rFCOh57opmrsdgVzKPKlw0goI7tykmNJjY+moq6JnQdrGJWb0u1zhhOfyK6QCJ3JlFUdYktJFRP7p3X/hEJIyMzMJDMzs0vv3bBhA2azmaysLJ+fWywWcnNzAXj99deZOnVqQNdwpSYuXryY2NhYTj75ZACmTp3Kgw8+yMGDB93X+vzzz0lOTmbEiBFdarseKa1qoKaxGYvZRH56QudvEARBiCAS2aUC3Kl83ZyQZyZZSU+Iwa5oZ1czVH5loEXvptD13bW423mwhqYWewjOGF485ea73/vsZCup8dG02D27jYKgRZQQetmN0Nh4GMropgEZCcREmamztbDvaF0IzhheQin+m0wmTUW2+RjUh+DuD9dYGqfgy6pVq1i4cCEbN25k9+7dvPrqq9x+++1ceumlpKU5xMvy8nIWLVrE1q1b2bBhA7fddhtvvfUWCxcudJ9n7dq1DBs2jOLiYvfP/vnPf7J+/Xq2b9/Ok08+ydy5c5k/fz6pqakAnHLKKYwYMYLLLruMjRs38umnn/LnP/+Zm2++GavV2pO/BlXj8uvKT48nJkqWkYIgqBsZpVSAEiLBx2QyebxaNDDJhVCbEjsm+Nucu/lqJ5QLnL5pcSRZo7C12Nl9qLb7JwwzSoiqzoHjc+9KZdRSCq8gtMaTyReC6CaNpbWH8lkQZTEzNFs7gk8o/RvBW/DRwKZXCA3qwTMP0EpKv+CL1Wpl2bJlzJgxg5EjR/Lggw9y++238+yzz/oct3TpUiZNmsS0adPYvHkzK1eudKcyAtTV1bFt2zaamjwFi9auXcvJJ5/M6NGjefbZZ3nmmWe49dZb3a9bLBY+/PBDLBYLU6dO5dJLL+Xyyy/ngQceCH/HNYSkMAqCoCUkjVEFhLoK1Tc7yjWzwLGH0LMrPz0Ba5SZ+qYWio7UMSBD3eHVoVzgmEwmhvVOYl3hUbaUVDE0R90VckIp9IHjc79q92FNLGwFoT1COR6O0FjBklD6N4LDt+vn4kq2lFRz6qjeoTlpmAhlwQ7Qlo+hbxpj6CK7tmkkhVXwZcKECaxevbrDYzIyMli1alWHx8ycObNNcY6XXnqp0+v379+fjz76qPOGGpg95Y4N1YGZInYJgqB+JLJLBRh5geMiVLv5Q5y7+Vrof6gFn2Eaim4KpUE9eDzLtHDfBaE9Quph6IzsKq6op7KuqZOjI489hH0Hj+ixWQNjgmsO0I2icD64ovo2a6Aap3frQqFLuVJYaxqbKTqi/hRWQdAahYcdYpf4dQmCoAVE7FIB7l3dUIhdfXyNedVOKD27ALcZ78/FlSE6Y/gIpSEzwGhX3/erv++h/MyDp++bD1Rp4nMvCP6wh9CzKzk2mrxecYBWBHDH95CNCX0dY8ImDTwLXANiKNJXAQZnO6pxVtY3qb4aZ6gju6ItZrfQqYV5gCBoDbfYpfLsCUEQBBCxSxUoIZzoFmhsVzOUkQwAYzS0wAmlITP4Lu7ULvi42heq+z44KxGr83PvmogJgtYIVzrb5gPqHw9DKfSBI8rZbHJUDjtY1RCak4aJUPfdGmVxp7L/pPLNj1CLnABjcrUzDxAELWFrtlPsFNDz09Vf5VcQBEHErgjjnWIQiolulMXsTmX8aX9F908YZkKZwgmeCJ+f9leoP30jxJFdLsGnWgOCT6gX9VEWMyP7yG6+oHFcY0KIVI/RGop0DaU5P0CCNcptoKz2/iuhDnXFs/nxU3FFyM4ZDkJVoMcbd981MAcSBC2x72gddgXiYyxkJkmFSkEQ1I+IXRHGp+x2iCa6Y90TPXVP8CH0oseQ7CRiosxUNTSzV+WRbaE2ZNaS4BPqFE6AMX1TAW187gXBH6FO6x6TlwrAxn0VITpj+FBCHN0EMDo3FVD/mBDqyC7wRDepPa3dNQcI7bPAFdklae2CEEoKneb0/dMTQrZmEQRBCCcidkUYH7+KEJ3TtejXwgIn1JEMMVEevw71L3Ac30M5YdCK4BNqoQ+05VkmCP4IdVq3a+Oj8HCd6k3qQ9138Igeahf/Q73pA57opp+LK1Ud5Rzq6G6AQZmJxEY70tr3qDzKWRC0ROFhxybygAxJYRQEQRuI2BVhfCsRhWiBk+fc1TxQSXOLPSTnDBehjmQArx1tAy5wRmlE8Al1Cid47eYfqKRFdvMFDRLqhX9qfAz9nb4qak9ns4dBkBnlTmtXt+ATDqHPFeVc3dBM0WH1RjmHo+/edg7i2yUIocM7sksQBEELiNgVYXwm+CGa6xVkJJJojaKhyc6OgzWhOWmY8FiVhMGrROV+HeHY0daK4BNqc36AgsxE4mMs1Nla2H1I3Z97QfBHqH2rQEPRns7voRTAR/ROxmI2UV7TSKmKTerDkcLpXZXwJxULPuHY8ALtfO4FQUu4/GAHiNglCIJGELErwnhrXaGa6JrNJh+jdjUTVsFH7X4dYYhuGpiZSFy0+gUfJQxhbRaziVF9tONXJwitCYfo4Upl3KDytPZwVOWLi7Ew2GlSr+YxIRwiJ3j7dlWE9LyhJBz3HSStXRDCgUvs6i+VGAVB0AgidkWYcBjUA4xxpjJuVPlELxzpbN5+HbvL1evX4Y5uCrXgk+vYzVfzvQ+HQT1oJ6pPEPwRjoW/J8KlInQnDQPhEPrAs/mh5v6HY9MHPOOhmp8FLkL9LPCOcla7nYMgaAFbs53io/UADMiQyC5BELSBiF0RRvFy7Qrtbn4qoO4JPngt7kJ4ziiL2b2rq+ZoBrtz/h3qijaue//j3qMhPW8oCUcaI8BYZ/W5H1V83wWhPTyiR+j+MkblJmM2QVlVI2VqTuVzfg/1eDguLw2AH/dWhPS8oSRc0U3jnePhz/sraVKp4BOuNMaBmYkkWaOos7Wwraw6xGcXBONxoKIeuwKx0WYyk6yRbo4gCEJAiNgVYXwtu0Jv1r21pJqGppaQnTfUeHbzQzvVndDfscD5och4gs9ELfQ9TJFdrr7/cqCKept6P/eC4I9wFK2Ij4liSHYSoHbxPzzRTRP6pwKOvqs1wscepufgwMxEkmOjqG9qYWuJOgWfcD0LzGYT4/qlArBexUKnIGiF/c6orr5p8SHflBAEQQgXInZFGG+D+lA+O3JT48hKstJsV9io5gVOmHa0J/ZziB7rVSz4hCuVzyX0bS+rprqhKaTnDhVKmNJ2+qTEkpMcS7NdUX1UoyC0JlxjwnjXol/F42G4fKsGZyW5I3y2lqpT8HER6vHQbDZ5bfwcCe3JQ4Q9HOHdTlybH2r+3AuCVth/1FHVtW9aXIRbIgiCEDgidkUYb/v0UE50TSYTk/IdE73/qniiFw7fKvASfA5WU1mvVsHH8T3Ufc9OjqVvWhx2BTbuU6dXi0fkDG3nTSaTJ7JNxWmcguCPcInAE/v3AtT9LLCHybPL4hPho87+hyuyC2BCP9d4WBHyc4eCcFThdKGFKGdB0AqeyC4RuwRB0A4idkUYxSurItQ72q4Fjponem7RI8R9z0i0kp8ej6KoN3UnXIbMoP5JfriEPtBGFIsgdESoF/6TnRsfP++vVG1aezjHBLVH+HjbGYQa9fc9PAIvwLi8VEwm2HukjkPVjaG/gCAYCE9kl1RiFARBO4jYFWHCZVAPMMk5yf1v4RG3H4raCOcCR+2+XeFK2wFUH90UrigO8BX6lHCuIgUhxISrKl+/XvFkJFqxtdj5uVid0Z7h8m8ELYyHju/h6PvYvFTMJiiuqKe0Un0FCsLl2QWQFBvNUKdfnVqj+gRBK0hklyAIWkTErgjjrUGFOqVrRJ9k4qItVDU0s/NQTUjPHTrCv8BR6452uBa24Eld+bHoqCqFznAKfSP7pBATZeZoXRN7ymtDfn5BCBce8T/06b2ezQ91jofhMOd34Yrw2XeknoMqrEgZzuimRGsUQ3OSAXUKPp7o7vAwQeXzAEHQCt4G9YIgCFpBxK4I4x15Euool2iLmXHO0uNqXeCEy6AePGLXj3uP0qJGwSdMC1uAYTlJxMdYqG5sZsdB9Qmd4UzhjIkyM9ZZjVStUX2C4A+3AB6Gc7s9HAvVblQe3ggfNY4J4RT6ACY6K1Kqs+8uoS88vXd7lqmw74KgFRqbWyirdmwUSGSXIAhaQsSuCBPOyC7wWuCotBJTOEUPVxWuWlsLW0qqQn+BbhLOhW2Ul9C5VoWL23AKfeDZzV+nwr4LQnuEM617Ur7Tw3GvSqM93els4Tn/RPeYoD7RI5wpnACTnP6dahwP7U7f0jB13e1X95OK/eoEQe2UVDSgKBAbbSY9ISbSzREEQQgYEbsiTLiqEbqYqPLUFc+aK/S/AIvZxOQBjkn+6t2HQ37+UGEO01/hlAHpgDr7Hu7P/bHuvqtvcScI7RFO/6KRfZKJjTZTUdfELhWmtYerWImLYwtUPB6GUeQEmFLgeA5uKq6kqkFd1Yndz4Iwnb9fr3h6p8Ria7FLKqMgdBHvFMZwbVIKgiCEAxG7Ik2Y/Som9k/DYjax90gdByrqw3SVrhPOyC6Aqc4Fzqpd6lvgeCK7wtP5qQMdfV+z+7DqjNrDvbCdlO/53Ber8HMvCP4I58I/2mJ2p3St3qNGETi8zwKX4LOltIqKOlt4LtJF7GGOdO2dEkd+ejx2Bdap7N6HU+AFx+9UzUKnIGgBVyXGPElhFARBY4jYFWHCWYUJHF4lo3Md/kVqFHzCnc7mmuSu3XNEdb5d4d7NH5uXgjXKTHmNjZ0q8+0Kd9+TYqMZ5fzcr1bh514Q/BFu0eO4gS7xvzws5+8O4R4TspJiGZSViKLAGrUJPmGObgLP5ofaBJ9w33eAYwtcEd7quu+CoBX2OcUuMacXBEFriNgVYcKdzgWeBc73Klz0u+SncO3mj+iTTFJsFNWNzWw+UBmei3SRcAt91iiL27NNfQuc8EZxgFdUn8r6LgjtEc6qfABTB2YAjo0Ptfl2earThm9Q8Ige6hoTwr3pBZ6NH7WNh0oYKzK7cPX9x31HqbeJb5cgBEuxM40xVyK7BEHQGCJ2RZhwp3OBZ0d31a5yFaazhTeVz2I2MUWlvl32HhB8XN5V6lvgODDiwlYQ2iPcoseYvikkxFg4WtfEllJ1Fe3oiQifqQUesU9VhFnkBI+H4+YDVVTWq8e3qyc0V5dvV1OLwvq94tslCMFSUumoxNg7JTbCLREEQQgOEbsiTLh38sFRiSnaYuJAZQNFh+vCd6Eu0DMpDOr07fKUmw+/0Ll69xFVCZ2uqJJw3vfJ+b2wmE3sP1rPviPq+twLgj88Y0J4iLaYOcYp/qttPOyJ6CaXb9fW0mqO1qrHt6sn+p6TEsuAjAQUlfl2uaN8wzgbNZlM7khf2fwQhOAprXKJXRLZJQiCtujS9OLJJ58kPz+f2NhYpkyZwtq1azs8/q233mLYsGHExsYyevRoPvroI5/Xr7zySkwmk8/Xqaee2pWmaY6eEHviYiyMdxoTqy2V0d4DYp9L7FpXeJSmFnv4LhQkPZHKN6ZvKnHRFo7U2thWVh2+CwVJTwh9CdYoxvRVr1+dILSmJxb+xzlTGdX2LOgJ36qMRCuDsxIBdYke7o2IMBc5cz0L1XTveyK6HTx9/26n+vzqBEHNKIoikV2CIGiWoKfUb7zxBnfccQf33Xcf69evZ+zYscyePZuDBw/6Pf7777/noosu4pprruHHH3/knHPO4ZxzzmHTpk0+x5166qmUlJS4v15//fWu9UhjhLsSkQuPb5e6Jno9kc42oncyvRJiqGls5se9FWG7TrD0hNAZE+WJ5Phmu3rufU+kcAJMH+RY2H+941B4LyS0SzCbI8899xzHH388aWlppKWlMWvWrE43U/SE0gMLf+8qreoS/x3fw/0snD5YfWNCT0R2ARzv7PtX2/3P1yJDzzwLpjn7vmFfBZV16knjFAS1c7SuCVuz41mRlWyNcGsEQRCCI2ix67HHHmPOnDlcddVVjBgxgkWLFhEfH8/ixYv9Hv+///u/nHrqqdx1110MHz6cv/71r0yYMIF//vOfPsdZrVZycnLcX2lpaV3rkcbweFaFl2nORf93O8vVVZXQPckP3yXMZpN7kr9ym3om+T1hyAwwY0gmACtVtMDpCaEPPH3/Zkc5zSpa2BuFYDdHVq5cyUUXXcSKFStYtWoVeXl5nHLKKRQXF/dwyyNDTxQsGdE7mdT4aGptLWzYVxG+CwVJT6T0g2dM+GrbIdWkdoc7fdXFtEEZWMwmdh2qVU1qd7grkLrITY1jcFYidgW+leguQQiYkkqHOX1GYgzWKEuEWyMIghAcQYldNpuNH374gVmzZnlOYDYza9YsVq1a5fc9q1at8jkeYPbs2W2OX7lyJVlZWQwdOpQbb7yRw4fbD7NvbGykqqrK50ureKoRhneiNz4vlaTYKI7WNbFxf0VYrxUM4Taod+Fe4GxXz25+Ty1wZg519H3dnqPUNjaH+WqB4UnhDG/vx+WlkhwbRWV9Exv3q6sapxEIdnPk1Vdf5aabbmLcuHEMGzaM559/HrvdzvLly3u45ZHB7tRjw7nwN5tNnDDYMSas2KoeAbyn9mCOLUjHGmXmQGUDOw7W9MxFO6EnUtoBUuKiGZ+XCqgnsq2nNj7Aex6gns+9IKidUmcKY46kMAqCoEGCErvKy8tpaWkhOzvb5+fZ2dmUlpb6fU9paWmnx5966qm89NJLLF++nAULFvDVV19x2mmn0dLiv0T0/PnzSUlJcX/l5eUF0w1V0VNeHVEWMye4InxUtMDxiH3hvY6r75sPVHGwuiG8FwuQntrRHpCRQF6vOGwtdtV4V/WU0BdlMXP8YFckh3o+90agK5sjramrq6OpqYlevXq1e4weNz/C/Xdx4jCn2LVNHYIH9NzGT2y0xe3f9JVK+q/00LMAfCPb1EBPRbcDzBjq2fRSS1SfIKgdl19XTrKY0wuCoD1UUY3xwgsv5KyzzmL06NGcc845fPjhh6xbt46VK1f6PX7evHlUVla6v/bt29ezDQ4hPeXVAXDi0CxAXQscew+JfRmJVkbnOszKv1aLd1UP7eabTCZmDnHce7VEtvXo4m6o+qL6jEBXNkda88c//pE+ffq0iQ72Ro+bH+F+HpwwOBOTCbaUVLl37SNNTxQrcaG2SN+eMOd3MdM5D/huZ7nbhyeS/P/27js8qip94Ph3ZpLMpAcSkhAIEHovUkIABTRKsWEviKisgIJiWRHWH+jqKiDiuiiK2AuIoAhWFOlICDVAgNA7KUBIJaTN/f0xmUkCAQlk7r0z836eJ4+7mZu554TJPfe+5z3vUatWG9h26LV4G0nPKSQlTT8btgihZ2lSnF4I4cKqFewKCwvDZDKRnp5e6fvp6elERkZW+TORkZHVOh6gcePGhIWFsW/fvipfN5vNBAUFVfpyXerf4G8/nq2b7CY1b3Tty/n0UrfLqsHyjRV7MnQxo63mg22fsr5vO57N6bxC559Q1IjJkyczd+5cfvjhByyWi99ku9Pkh1pLukIDzHSoHwLAcp1cD9FgLFh/MFMXS7vVyvIFaBMVRKi/D/lFpWw6fMbp5/s7atVqA1tWX5w9q08ngU4h9C5VljEKIVxYtYJdPj4+dO7cuVL9FHs9lbi4uCp/Ji4u7oJ6K0uWLLno8QDHjh3j9OnT1K1btzrNc0lWdRKbAKgTaKZ9fVt2kx6WMFQMuqiyhKFCsXI9FOkvL0bt/N7HNQnFx2TkaGYBB0/lO/18f6d8uZbz+x4eZKFV3SAUxfZvL9RxJZMjdm+99RaTJ0/mjz/+oH379pc81p0mP9QMAl/fsizTVyfL2tXse8Wl3esOaL+0W826VUajwbGsXw8BHzXHAtDfMk4h9C4tx1agXjK7hBCuqNrLGJ977jk++ugjvvjiC3bt2sUTTzxBfn4+jz76KAAPP/ww48ePdxw/ZswYFi9ezLRp00hJSeGVV15h48aNjB49GoC8vDxeeOEF1q1bx6FDh1i6dCm33347TZs2pV+/fjXUTf1SM7MJypcwrNDBjV7FBCM1+l+xWPmWI3qY0bb9V41/eX+zF11jbDucLtPBw61aBZnt7Jkceui7p7iSyRGAN998k9dee43FixfTpUsXNZqqG2o++PetsJytsKTq+phqUnNps8FgcAQ99HBNsKp8PSzve/rfHOl8agb6oPweaMOhTLILitU5qRAuTDK7hBCurNrBrvvuu4+33nqLiRMn0rFjR5KSkli8eLGjLsuRI0dITU11HN+jRw/mzJnDrFmz6NChA9999x0LFy6kbdu2AJhMJrZt28Ztt91G8+bNGTZsGJ07d2b16tWYzeYa6qZ+qTmbDdC37KF/1Z6TmtfrsFbM7FKh/14mI33Lshl+33F5NYOcSc16bQDxrWx/o3/s8LwHHHvfl6dk6OLB3lNUd3JkypQpTJgwgU8//ZRGjRqRlpZGWloaeXn62DXP2dQMerSJCiIswEx+USkbDuog+K9i3SqAG1vbsgv/2JmOVQeZvqBedlPfFuF4GQ3sSc/jwElt/7bK74HU6XujMH+ahQdQYlV0k9UohF4pilKhZpcUqBdCuJ4rKlA/evRoDh8+TGFhIYmJicTGxjpeW7FiBZ9//nml4++55x52795NYWEhycnJDBw40PGar68vv//+OxkZGRQVFXHo0CFmzZp1QVFjd6XmbDZA+/ohhAWYyS0sIUHj5RsVHy/U6v+AtrYHnMU70jSvXaVmrRKAfm1sfd9wOFPzmm1qP+B0ig4hPND2uV+rkx0pPUF1J0c++OADioqKuPvuu6lbt67j66233tKqC+pScTwwGg3cUBb8X7wj9W+Odj61g/9xjUMJtHhxMreQzRpn+tqDbUaVtgwK9vMmromtdtXvGk9+qLUjc0X97fcBydpPegmhZznnSjhbZJsgjAySzC4hhOvRxW6MnkzNbbcBTEYD/dvaHjR/267tA47amV0A1zWvg8XbVrtqx4kcdU56EWovYY0K8aVDdAiKAkt2avyAo3Jml9FocAT7Fm+XBxw1VWdy5NChQyiKcsHXK6+8on7DNaD2crYB7ewP/ema1zFU+5rg42V0ZHxqHfRQt3qljeN6qHGWs9rZ7VDe9xV7MigokkxfIS7GntUV4ueNr49J49YIIUT1SbBLJ9QKeAAMbGsr/P/7jjRKSrVbyqh2zS4APx8vR70SrZcyqr1sB6B/G33MaKudxQHls/lLdqVr+rkX4mIcNbtU+rPo0SSMIIsXp/IK2XgoU52TXoTadfygcsBHy0xftYOcADe1jsBggK1Hs0jNLlDvxOfTYCxoExVE/Vq+nCu26qJIvxB6lZZTVq9LsrqEEC5Kgl0aU3s2G6BbTG1q+/tw5mwxiQe1e8Cp+GyhZsBnQFmwTy8BHzU736+NLZMhYf9pss9qV5xXi0Bft5jahPh5k5lfxIZD2tcoEuJ8ai9r9/EyOmpX/aaT7Ca16laBrVC7xdvIsTPaZvpqcR8QHmShcwPbpiVa1nFUO7sdbH9f9omfP3RQv1MIvUrLlp0YhRCuTYJdGiuf0VXvVs/LZHQEPX7VcCmjgvrLGAH6tgzH22Rgb0Ye+zK0K86raPBv37hOAC0iAimxKvy5S8OljBrM5nubypctaZ3VJ0RVtHjwH9jOHuxK1bRQu2NZu4qd9/Ux0ae59puWlNetUvNfXh+1q9QO8NrZ+/7nrnTNN+sRlTVq1AiDwVDpa/LkyZWOmTdvHh07dsTPz4+GDRsyderUv33fzZs3c+ONNxISEkJoaCjDhw+/YPOT889rMBiYO3dujfbPlchOjEIIVyfBLo1p9WgxoMJSRq1qtWixjBEg2Nebnk3DAI2Dfeo/2wHlN/m/aNh3Leq0QPkGBb9uT9W8RpEQ59Piwb9XszACzF6k5xSy5ah2GY9q1zC0s18Pf96WqtlSRkWDICeUL+NMPHiajBxtNi3Raiy4pkEt6gSayTlXwuq9spRRb1599VVSU1MdX0899ZTjtd9++43BgwczcuRIkpOTef/99/nvf//Le++9d9H3O3HiBPHx8TRt2pTExEQWL17Mjh07eOSRRy449rPPPqt07kGDBjmhh64hI7cQgPBACXYJIVyTBLs0ptWNXlyTUEL8vDmVV8Q6jXZltGpYI+XW9lEA/LDluHYPOGX/NapZqAW4tYOt7yv3nORUXqGq57Yrr02kbt97NQsj2NebjNxC1u4/peq5hfg7WtStMnuZiG9ly276aasOAuAqnze+dQQWbyMHT+WTdDRL5bPbaJXdFF3bj2sahGBV4MetJ1Q9t51WWW1Go6HSfYDQl8DAQCIjIx1f/v7+jte++uorBg0axMiRI2ncuDE333wz48ePZ8qUKRe9n/v555/x9vZmxowZtGjRgq5duzJz5ky+//579u3bV+nYkJCQSue2WDw30JORY7tHrBNo1rglQghxZSTYpTGtZrO9TUYGtrNld32/+Ziq57areEuixWy+r7dJ0wccrR7umoYH0KF+MKVWhZ80esCxapTVZvYycWsH2+f+h83ygCP0RYu6VQC3dbQ99P+49YTmS7qMKt+VBJi9HBlOWgU9tJr0ArjjmvoALNDoeqhVVhvAndfUA2y7E+ec066GpbjQ5MmTCQ0NpVOnTkydOpWSkhLHa4WFhRcEoHx9fTl27BiHDx+u8v0KCwvx8fHBWOEC4+vrC8CaNWsqHTtq1CjCwsLo1q0bn3766SUnRAsLC8nJyan05U5O5kmwSwjh2iTYpTFFw5vcu8puchcnp5FfWPI3R9c8pcIzldr99zd7OZavaHeTb/uv2rP5AHd0st3ka/Vwp+Xn/o5Ots/9bxp97oW4GK2CHtc1q0NYgJnM/CJW7M5Q9+RlyoP/2l0Pf9Io2KdVkBPglnZ18TYZ2Jmaw+60XNXPr0Vxfrs2UUE0Cw+gsMTK4u1Sx1Evnn76aebOncvy5csZMWIEb7zxBmPHjnW83q9fPxYsWMDSpUuxWq3s2bOHadOmAZCaWnV26vXXX09aWhpTp06lqKiIM2fOMG7cuAt+5tVXX2XevHksWbKEu+66iyeffJJ33333om2dNGkSwcHBjq/o6Oia+BXoxinHMkYJdgkhXJMEuzSmVQo/wDUNQogJ8+dsUakmtasqFqjXov+OB5xtWj3gaBfwubVDFF5GA9uOZWtSpF+rjEawfe4bhfpRUFwqheqFrmj14O9lMnJHJ1t2l2aZvhoGPXo1DSMswMyZs8Ws3KN+/SarBstX7Wr5+9CnhW0Z64It6v/ba7Wk3X7OQWX3AVr03ZOMGzeuyuLvFb9SUlIAeO655+jTpw/t27dn5MiRTJs2jXfffZfCQlvg5fHHH2f06NHccsst+Pj40L17d+6//36ASplbFbVp04YvvviCadOm4efnR2RkJDExMURERFT6mQkTJtCzZ086derEiy++yNixYy9Z/H78+PFkZ2c7vo4ePVpTvzLNKYrCyVzJ7BJCuDYJdmlMqyLlYLvRu6ssjV+LB5yK9cG16H/PpmGEB5rJOlusSTaDtSy+pkXAJzTATO/mdQD4QZMHHO0CfRUfcKRWi9ATq4ZB4Ls62zIel6VkkJlfpPr5y7Pa1O+7l8nIoI72+k0aBD00DPQB3Fl2PVy05YTqG3dotZzfzj4WrDuQyfGsAo1a4f6ef/55du3adcmvxo0bV/mzsbGxlJSUcOjQIcB2jZgyZQp5eXkcPnyYtLQ0unXrBnDR9wB48MEHSUtL4/jx45w+fZpXXnmFkydPXvJnYmNjOXbsmCPQdj6z2UxQUFClL3eRU1BCUantRjUsQIJdQgjXJMEujWlZqwNs9ToMBtuN3tHMs6qeu2IdBC36bzIauL3sAWfeRu1mdbW6yb+zQq2WklJ1M9vsgT4tHmwB7ixbyrhm3yl5wBE6ot140DIyiLb1giguVfgxSf0gcHm2p+qnBuCOsomfP3eqH+wrz+zSpvPXtwonyOJFWs451Xcm1DLLF6BeiC/dG9cG4DsN7wPcXZ06dWjZsuUlv3x8fKr82aSkJIxGI+Hh4ZW+bzKZqFevHj4+PnzzzTfExcVRp06dv21LREQEAQEBfPvtt1gsFm688caLHpuUlEStWrUwmz0v2HMyz7ZDa5DFC4u3SePWCCHElZFgl8a0rNsEthu9Hk1CAZi3Ud3060qZXRr1/76utvoKy1LSOaFy0EPrB5z41uHU9vchNfscy1LUzWxzZHapetZyDUL96NEkFEWBbxKPaNQKISrTMrMLyus4frvxmOq71GpZtwqgdd0g2tULpqjUynyVx0KlvPOaMHuZHJMfX69T93qo9YQfwAPdGgDwzfojqk/8iMoSEhJ455132Lp1KwcOHGD27Nk8++yzPPTQQ9SqVQuAU6dOMXPmTFJSUkhKSmLMmDHMnz+fd955x/E+69evp2XLlhw/Xh64f++999i8eTN79uxhxowZjB49mkmTJhESEgLATz/9xMcff0xycjL79u3jgw8+4I033uCpp55S81egG7IToxDCHUiwS2NabDV/voo3eoUlpaqd1x7w0LLvTcMDiWscilWx9V9NWtaoAdsDzr1dbMG+r9ZVvYORs2j9UA/wUPeGAMzdcETzHeiEAG13pgNbHUOzl5FdqTlsOnxG1XNrPRYaDAaGlF0TZicewaricj4ta3fa2a+Hy1LSNcl21bLv/dtGEurvQ1rOOZaqPPEjKjObzcydO5fevXvTpk0bXn/9dZ599llmzZpV6bgvvviCLl260LNnT3bs2MGKFSscSxkBzp49y+7duykuLt9lc/369dx44420a9eOWbNm8eGHH/L00087Xvf29mbGjBnExcXRsWNHPvzwQ95++21efvll53dch2QnRiGEO5Bgl8a0ns0G6NcmkoggM6fyilicrF7Bbq2z2uyGxNlu8r9Zf1TVoIdV5cyJqgyObYDBAKv3nuLgqXzVzqt1oA/gxtYRhAeWfe6lUL3QAavG18QQPx8GdbQt5/t87SFVz62Ha8KtHaIIsnhxJPMsq1Rczqd13SqApuEB5RM/Kma76iGzy+xl4p6yiZ+vVZ74EZVdc801rFu3jqysLAoKCti5cyfjx4+vtIwwLCyMhIQE8vLyyM/P588//yQ2NrbS+/Tp0wdFUWjUqJHje19++SWnT5+msLCQrVu3MmTIkEo/079/f7Zs2UJubi55eXkkJSUxYsSIixa9d3cnHTsxWjRuiRBCXDnPvILriB5u9LxNRgbH2gI+X6j4gKNlcf6KyoMeharuzqeH2fzo2n70LduJa7aqN/naZ/V5m4yOrEZ5wBF6oOhgPLAH/xcnp5Gec06181q1XssH+PqYuLuz+kEPretW2dn/7eduUG/iRy+TXlpN/AihV7IToxDCHUiwS2N6udG7v1s03iYDm49ksf1YtirnLF/GqG3fvU1G7i8LenyZcEi18+rhwRZwLN2Zv+kYZ4tKVDmnI4NF41DnA90aYDIaWH8wk5S0HE3bIoQeAuBt6wXTpWEtSqwKc1TM8Cnvu2qnrNLg7raxYFlKhmqbtuhlLKg48fNbcqoq57TqZNIrurYffcp2KP4qQSY/hJBglxDCHUiwS2N6WL4AtjTlge3qAvDZXwdVOadV+4l8hwe7NcDbZGDDoTOq1arRy2z+dc3r0DDUj+yCYr7doE5hZr083EUGW+jfJhKAWSsPaNsY4fH0ku36cI9GgK121blideo4ar2E065JnQCubRaGVYGPV6tzTXAMhTqY+LFneX+48oAqmxRoXautoqFln/u5G46QdVbdHTmF0BtHza4ACXYJIVyXBLs05pjN1sG/xGM9YwBYtPWEKjPaerrJjQy2cEcnW62amSv3q3JOPSxhBTAZDQy/rjEAH606QLEKu1Hp5cEWYERvW98XbT3BsTPqZHIIURW9BIEHtI2kbrCFU3mFfLfpmDon1dF48ETvJgB8u/Eop8se+JxJL5NeAA/HNcTX28TO1BxW7z3l9PPpJbsdoHfzOrSMDORsUalkdwmPJ7sxCiHcgQ5CLB5OJ8u5ADpEh9CraRilVoVZq5w/o63oqO8Aw69rgsEAS3amszc91+nn08uyHYC7rqlPWICZE9nn+DHphNPPp6e+t69f/rn/eLU6WY1CVEUPu5SCLcPHHgCfuXK/ygFwp5/qb8U1CaVD/WDOFVtVqWOph+L8drX8fRy1DD9Y4fyJH3s5Ax10HYPBwBN9bIHOz9YeoqBIvd2phdAb2Y1RCOEOJNilMauOZrMBnuxbPqOdkevc4sTly/iceprL1jQ8gJtaRwAwU4UlbVYdreO0eJsY1suW2Tdz5f4KbXMOvWSw2NkfcOZuOKJKJocQVVFw/rKxy3V/1waE+vtw7EwBP21VIwBuvyZof1EwGAyMLMvu+iLhMHmFzq1lqJcgp90/ro3By2gg4cBpth7Ncuq59JTlC3Bzu7rUr+VLZn4R8zeps6xfCL0pLrWSmW9byivBLiGEK5Ngl8bKN6DSx41eXONQrmkQQlGJlU+cnOVSvoxPH30HHA84i5KOc/i0c3dk0lN2E9gKMweavdibkef0XSn1ltXXo0ko7csyOT5eI9ldQhuOoIcOLgq+PiYeKwuAv7/C+QFwa1nymPY9t7mpTSSNw/zJLihWYUmbfrKbAKJCfLm9o21Z//Sle516Lj1ltQF4VcxqXLFftZp1QujJ6TxboMtkNFDbz0fj1gghxJWTYJfG9JbZZTAYGNW3KQBfJhwmw4lbz+sszgdApwa1uK55HUqsCv9dsse5J9PZjHaQxZtHyx5u3/pjNyVOXLqkl3pldgaDgaevbwbYNmhw5udeiIvSSYF6uyFxDQm0eLEvI48fnZzdpYedKCsyGQ2Mvt42Fn6wYh/ZZ4uddi57oE8PQU67UX2bYDIaWJqSwcZDmU47T/muzE47RbXd2yWayCALJ7LPMVvFHUmF0Av7ToxhAT66ui4JIUR1SbBLY/pZyFbu+pbhdGoQQkFxKf/903mzunoqylvR2H4tAFvB8p0ncpx2Hj32//FrY6jl583+k/l8v9l5han1VJTY7oZW4XRuWItzxVb+5+RsBiGqUj75oY+/iyCLtyPb9a0/dlNY4rwsF70tbQa4vWM9mkcEkHOuhA9XOa9+lZ6Wr9o1rhPAvV3qA/Dm4t1O25nRqrMsX7At638m3jb5MWP5PqcvYxVCb07m2Sb8ZAmjEMLVSbBLY4oOl/IZDAbGD2gFwLyNR9mXkeeU8yg6WrJTUdt6wdzSvi6KYnvAcxa9ZTIABFq8HZl97/y512lLOPSW0Qi2z/2L/VsCMHfDUQ6ecu4yViHOp8ds18d6xhARZObYmQKnLucrr+Gon86bjAZe6Ge7JnzqxIxPPfYd4OkbmuHjZWT9oUxW7DnpnJPYxwKd3Y3e3bk+jcP8ycwv4uPVzq/hKYSeOHZiDJBglxDCtens9sLz6K1Iu123mNrEt4qg1Krw5uIUp5xD0WFmk93zN7XAZDSwLCWDtfuds/263pby2T3UvSFRwRZSs8/x6V/OqV9VntGor853i6lN3xZ1nPq5F+Ji9HhN8PUx8Wx8cwDeW76P7ALnLOfT0658FcW3CueaBiGcK7bytpOWtutpJ8qK6gb78kiPRgBM+S3FKUvb9ZjZBbbaXc/fZMvy/mjVAdJlabvwIKfLitOHSrBLCOHiJNilMb3e6AG82L8FRgP8sTOdNXtrPuBTnsWgv77HhPkzONa2/frLi3ZQ7ISbfL0V5rWzeJv4Z9lSzveW7eNEVkHNn0SnQV6AFwe0xGQ08FtyGquclc0gRBX0tnGD3d2d69MsPICss8VOq2Woo81pKzEYDPxroC3T+duNR0lywu6EeqxbZfdE7yaE+HmTkpbLV+tqPrNPj8tX7Qa0jaRjdAj5RaW88esurZsjhGrsBepD/aU4vRDCtUmwS2Pl261r3JAqNIsI5OG4RgBMWJRc40va9JrVZvf8jS2o7e/D3ow8PnNChpMe61bZ3dGpHl0b1eJsUSmv/1LzN/l6zGCxaxkZxMNxDQF45ccdTq1TJIRdxZpIersmepmMTLy1NQBfJhwi+Xh2jZ9D0Vm9soq6NKrNndfUQ1Fg4qJkSmt4Z0q9BjkBavn7MLZsKefbf+whI7dmM5ysOh4HjUYDr93eFoMBFiWdYN2B01o3SQhVZObbljHWlmCXEMLFSbBLY3rN7rF77qbm1Ak0c/BUPh+urNm6FVZFp1P5ZYL9vBk3wHaT/86fe0nNrtkMJz3P5hsMBl69vS0mo4FftqfWeIaTnrP6AJ69sTlhAWYOnMrn49XOWcopREUV63/r8e/i2mZ1uLVDFFYFXvphuxMDPvo0fkArAs1ebDuWzTfra3aHPj1nNwHc1zWaDvWDyS0sYdKvNbu8u7x2ZY2+bY1pVz/YkeU9cVGyU7K8hdAb+zJGCXYJIVydBLs0prfdt84XZPFm4i22Gf0ZK/ax/2TNFavXe2YXwN3X1KdzQ1uG078WbK/RHan0vIQVoFXd8gynf/2wvUZ3pNLjTpQVBVm8eelmW6Dzf0v3si8jV+MWCXdX8cqi17+LCTfbAj5bj2XzdQ0vaXMEPXQ6INQJNPP8TbbaZVN+S6nR5d16zm4CW6H+V8synH7YcpzluzNq7L31XLvT7p83taCWnzd70vN4f7nzduUUQi8yHTW7JNglhHBtEuzSCZ3e4wJwS/u6XNe8DkUlVp79NqnGZjb1vJTNzmg0MOnOdviYjCzffZJvNxytsfdWdLgj4fmev6kF9Wv5cuxMAa//srPG3levu49VNKhjPcfn/rl5W2VGXziVtdIyRn3+XYQHWRjb31bPb/JvKRyowckPvQfAwbZ5R6cGIeQWljD2u21Yayi7Te/ZTQAdokN4tEcMAC9+t42ss0U18r6uMBaE+Pnwym1tAHh32V6nLOMVQk8yHZldUqBeCOHaJNilMb1ndoFttnnKXe0IstiWcLy7dG+Nvr+e+w7QPCLQMaP/2s87OZp5tkbeV68FmSsKMHsx9e4OAHyz/ijLU2pmRl/vy3bA9rl/8672js+9zOgLZ1JcIbULGBzbkJ5NQykoLq3RyQ891zC08zIZmXZPByzeRtbsO8XXiTWT3eYKgT6Asf1b0LiOPxm5hbz8444aeU+r3tevlrmtQxQ3t6tLiVXhuXlJNV7DVAi9UBSlfDdGWcYohHBxEuzSWA2uinOqusG+vH5HO8C2/fzGQ5lX/Z6ucoMP8I9rG9O1US3yi0oZM3cLRSU1l+Wj92BfXJNQHutpm9F/4bttZNTAFuzlNbuu+q2cKjLYwqu3twVsM/o18bkXoipWHReor8hoNPDWPR0IstiWM06vockPV8j0BWhcJ4DxA2y7M77x6y5S0nKu/k1dINAHtp163763I8aygu0LNh+76vcsn/PRd98NBgOvDWpLWIAPe9LzZHdG4bbyi0od97hSs0sI4eok2KUxvdfqqOjWDlEM6mgrUDxqzuar3pXJFWby7UxGA9Pu6UigxYvNR7Ku+ka3Yu0v/ffeNqPfMjKQU3mFjJqz+aqzOcofbPXf+9s7RnFrhyhKrAqj5mzmZG6h1k0Sbk7vfxfnT34sS0m/6vd0heVsdkO6N6R38zqcK7byxNebyTlXfFXvZ3WBJe12HaNDePqGZoCtluPVBvtcoXanXW1/H0em85cJh1mUdFzjFglR8zLzbFldZi8jfj4mjVsjhBBXR4JdGnOFuk0V/eeOdjQLDyA9p5DRs7dcVdDDVWby7RqE+vH2vR0B+Hztoau60a1Y6kXvD7Zgm9H/4KHOBJq92HDoDJN/u7oduVxk5Qpg+/eZfGc7mpZ97p/6ZjMlUr9L1DBXyeyyu7VDFA91b4CiwJi5SRw8lX9V7+cKhcrtjEYD/72vI/VCfDl4Kp8X5m+9qs1LFFdJdS3z1PXNuK4s2Dfyq01XFexztfuAvi3DGd23KQDjvt/OnnTZvES4l9P5tgm9UH8fl7g/FUKIS5Fgl8Zc6aEfbDWcZg6xBT3WH8rklR93XPFNfnlRXlfpPdzYOoIn+zQB4MXvt7HlyJkreh/FxR5sAWLC/HnrXtus9idrDjJ3/ZErfi9XyuIA8Dd7MfOhzvj7mFh3IJOJV/G5F6IqFT9Oel/SZTfxljZ0bliL3HMlDP9yI9kFVx70cLXxoLa/D+8PvgYfk5Hfd6Qz7Y89V/xeCq416WUyGninLNh36PRZRs2++mxfV/l3B3j2xuaOunXDvtgg2b7CrTiK08tOjEIINyDBLo2V3+S6zo1ekzoBvH1fRwwGmJ14hPdXXFnhblcoUl6V529qQd8WtlntYV9s5NAVZDRYXfDBFqBfm0ievt42q/3SwuQr3oLe1WbzAZqGl3/u51zF516IqlTM7HKVvwsfLyPvD76G8EAzezPyGP7lxisu3O2K14QO0SH85w5bTb/3lu9jTuKVTQA4yhm40FhQ29+HD4d0xs/HxOq9pxi/YPsVTQDYd7R0pX93k9HA9Ps70TDUj6OZBfzjiw2cLSrRullC1AjZiVEI4U4k2KWx8rpV2rajum5sHcHLt7QGYOrvu5m34Wi138PVstrsTEYD7z14De3qBZOZX8TQz9aTXs2i7QoVHmxd7K/w2Rubc+c19Si1KoyavZnNV5jdBq4V5AVbsO9qP/dCVKXSZowu9GcREWThs0e7EmD2IvFgJs9+m0SptfpBD1dNlLy3S7SjhtWERcn8viOt2u/hSnWrKmpbL5gZD16DyWjgu03HePP33dUOeJWv4HStzocGmPn80W7U8vNm67FsRs3eTGGJ7NAoXF+m7MQohHAjLvaY7X5cqUD9+R7pGVO+pG/Btmo/+FtdbClbRf5mLz55pAvRtX05fPos989aR1r25Qe8Ki9Zci22GlbtubZZGGeLSnn4k/VsOly9gJcr7cR5vkd6xjD8usbAlX3uhaiKUmEVmKtdE9tEBTNrSGd8TEZ+S07jmW+Tqr2szdWWMVb0bHwz7u5c3zEBUN2Al6tmOYOthtV/Btmy2z5YsZ+3/qhewMuVx4KYMH8+HtoVs5eR5btP8sTXEvASrq88s0uCXUII1yfBLo3ZM3xc8UYP4IV+LRxFisd+v42v1x2+7J9VXDW1q0x4oIU5/+juKFJ8/6wEUrMLLutnKz4LuOLDnY+XkQ+HdCaucSh5hSU8/EkiGw9lXvbPu9JOnFUZP6AlD8c1dHzur6Z+mRBwXranhu24Uj2ahjH9gU54GQ38tPUET83Z4ti+/nK4csDHvonFbfZdW2dvZnFy6mX/vKtmN9k90K0BE8syXmcs38/UamR4uVr9xvN1bliLTx+xBbyWpWQw8qtNV7yUVwg9OC3BLiGEG5Fgl8ZcObsJbDfnr93elkd7NgLg/xYm896yvZd1o+vKM/l20bX9+HZEd+rXshXqvfP9tew88fdbsbtifZ7z+fl48ekjXenRJJT8olIe+iTxsh/wXLE+T0UGg4F/39aGR3o0AmDcgu1MX3p5n3shqlIp29NF/zD6t41k5kO2DK/FO9L4x5cbL3unPlcPeniZjLx9bwdu72gLeD05ezNfJRy6rJ915ewmu8d6xTgCXu+v2M+477dfVnafKwc57Xo2DePTR7pi8bZleD30caIjO0YIVyPLGIUQ7kSCXVpzgxs9g8HAxFtaM7K3bUnjW3/s4bl5W/92dtMdbvAB6tfy49sRcTSp409q9jnumbn2bwu3VwyJuOrDHYCvj4lPhnZ1FOx/YvZmZq3a/7dBH3cIdBoMBl6+tTUjetuWNL69ZA/Pz99arWwWIewqBsBdrXZTRfGtI/hoaBcs3kZW7TnJ3R+s5Wjm2b/9OVcPgIM94NWRe7vUx6rAhEU7+M/PO/+2hpmrB/rsHusVw38GtcVogG83HuWRz9b/7Q6d7jAWgC3g9dkj3Qi0eLHx8BnufP8vDl7B5jVCaE0yu4QQ7kSCXRorr9mlbTuulsFgYNyAlvxnUFtMRgM/bDnO/bPWXfohx01u8AHqhfiy4ImexDW2ZTkN+3wD//tz70UfcqxulAHk62Pio4e7OJb1vfFrCk/PTSL3Ehkdrroxw/kMBgPjB7RyfO4XbD7OvR8mXNbDvRAVVS5Q79p/GL2b12H+iB5EBJnZk57H7TP+YtWek5f8mfKgh/Pb50wmo4Epd7XnhX4tAPh4zUEe+Ww9J3MLL/oz7hDos3uoe0M+HtoFPx8Tf+07ze3vrSH5ePZFj3ensTCuSSgLnujhyPS+7b01LE6u/oYFQmgpM992rQoNkGCXEML1SbBLY+Up/G5wl4vtRveLR7sRZPEi6WgWA6ev5pdtVS9tc5dAn12wnzdfPNaN+7pEY1Xgv3/uYcgniWRUsVOjq9fsOp+Xyci/b2vDxFtaYyqr2XPruxd/yFHcJKvP7qHuDflkaBfH5/7m6aurVbNHCHcKeAC0qx/MolG9aFsviMz8Ih7+dD2Tft110cxHq2NiwPV/AQaDgVF9m/LuA52weBtZvfcUA6evZu2+U3/zcyo10MmubxnBvBFxRAVbHMv7v0o4VGXGr7tMfNg1iwjkhyd7ck2DEHLPlTDy60288uMOqeMlXEZmnj2zy6xxS4QQ4upJsEtj7nN7X65XszB+efpaOpXd7I2as5mnv9lywcy2ozi/u9zlYivcPuXu9ky7pwO+3ibW7j9N/Nsr+XbDkco3+pXq86jfTmcwGAw81iuGeSO6Ox5yBs34izcXp1xwo+/Ku5BeTJ8W4fzy9LV0jA4h51wJI7/ezFNVfO6FqJJr79dRpchgC9+N7MGQ7g0B+HDVAW6f8RdJR7MuONZdMrsqurVDFD+N7kXziABO5hby4MeJ/OuH7RfUMbMHOt1h4sOubb1gfh1zLfGtwikqtTJh0Q4Gf5zI4dOVl/a5yzLGiuoEmvl2RBwjynbt/XztIQZOX836g5e/iYsQWjhXXEp+ke1+TZYxCiHcgQS7NObqBeovJrq2H/NGxPFknyYYDfDj1hPcMG0F36w/4ljaZ3XDhzu7uzrX56enetGuXjA550p48fvtPPDROvak5wLnFajXqpFO0rlhbX4dcy0D20VSYlV4f8V+BvxvNav3li9jKg90atVK54iu7cf8kXGM7G373P+09QTxb69kboXPvRBVcdexwOJt4rVBbflwSGdC/LzZlZrDHe//xcRFyZXqObn6Dq0X0ywikEWjevFgbAMA5iQe4ca3V/LLtlTHBIgbreSrJMTPh48e7sKEW1pj8Taydv9p+r2zivdX7HNMgLhL7c7zeZuMjB/Yik8f6UKdQDMHTuZz74cJ/OuH7VK8XuiW/bPpbTIQZPHSuDVCCHH1JNilMXfYiehivE1GxvZvycJRPWkTFUTOuRLGL9jOzdNXs3RXevlstpt+CpuGB/DDkz14aWArLN5G1h3IpP87q3jxu22kZpcvbXS3h1uwPeS8P7gzMx/qTHigmYOn8hnyyXqGfJLIjhPZblOQuSreJiPjBrRk0ahetK4bRHZBMeMWbGfg/1azPCVDdmwUVXLXALBdvzaR/Plcb+7sVA9FgS8TDtN76nI+WnWAc8Wljr8Ld8rssvP1MfHGHe345vHuxIT5k55TyKg5m7nzg7WsP5jp1tdDg8HAsF4x/P7MdfRoEsq5YitvLt7NDdNW8sOWY47lq0Z3/IfHtqTzz2d7c3/XaMAW7Oz95nJmLN9HQZEsbRT6Yg921fLzcbuJByGEZ3LTMIPrcOebXLv29UNYNKon/3dzKwItXqSk5TLsi408+tkGrZvmdF4mI49f15g/nulNvzYRWBXbLlUDp692HOPG//T0bxvJn8/35tGejfA2GVi99xS3vLuGY2cKAPebza+oXf1gFo22fe6Dfb3ZnZ7Lo59v4O6ZCfyxI61CjSIh3HNp7/nCAsy8fV9HZv8jliZ1/Mk6W8zrv+6i71srHEtnDG58VYhrEspvY65lzA3N8PU2seVIFvd+mEDCgdOAe48FDUP9mf2PWN66pwORQRaOZxXw7Ldb+Wj1QcC9x4JgP28m39Webx7vTuu6QeQWljD19930nrqcD1bs/9sdK4VQi+zEKIRwNxLs0phjNl/jdjibl8nIP65tzOqxfRnZuwlmr/KPnrXqesVupUGoHx8O6cL3T8TRpWGtSq+588MtQJDFm5dvbcPS5/pwW4eoSkt2TG46m2/nXfa5X/VCX0Zc1xgfLyObDp9h+FebiP/vSr5ad/iC+j3CM7nbpg2X0rNpGL8/cx1v3t2eqGBLpUxXN78cYvE28eyNzVk5tg+DYxtUuga686QX2Ma6uzvXZ/k/+/BCvxYEmsuXSblrZldFcU1C+fmpXrxzX0fqhfiSkVvIlMUp9Jy8jP/8vJP9J/O0bqLwcPYao3UCpTi9EMI9yIJsjXnCbH5FIX4+jBvQkkd7NuKTNQeZt/EofVrU0bpZquncsDbzR8axZt8pPvvrEA1D/bRukmoahPox/YFOPNm3CbNWHiDnXDHNIgK0bpYqgv28GT+wFcN6xfD52kN8ve4wB07mM2FhMv/5eScD2kZyV+f6dG8cirdJ5iA8kSdk+VbkZTJyb5dobusQxfyNR/lkzUEMBgMRQRatm6aK8EALr9/RjhHXNWHmqv0cOJlH10a1tW6WKnx9TIzq25SHYhvy+dpDrNyTQf82kVo3SxVGo4FBneoxsF1dftx6glmr9rMnPY+P1xzk4zUH6dKwFvd0qU//NnUJ9vPWurnCw6SX7R7uKddhIYT7MyhuUEAmJyeH4OBgsrOzCQoK0ro51fLBiv1MWZzC3Z3r89Y9HbRujhBCBXmFJczbcJS5G46wJ718Nj/Y15sbWoXTr00kPZuGEWDW33yE3q+3em/fxRw5fZbrpi7Hz8fEzlf7a90cTSiK4jETP0KA7TO/YvdJvlp3mBW7MxwToF5GA90bh9KvTQQ3tIogKsRX24ZWwVWvtc7k6r+TCQuT+WrdYUb3bco/+7XQujlCCHFRl3u91d+TlIdx152IhBAXF2D24rFeMTzasxHbjmUzb+NRfktOIzO/iAWbj7Ng83G8jAY6RIfQo0ko3RuH0r5+MIEWmel3V56ypP1SJNAlPI3BYKBvy3D6tgwnPecc328+xsItx9mTnseafadYs+8UExbtICbMn7gmofRoEkrnhrWIDLLI34uocWn2zK5gyewSQrgHCXbphKcsXRFClDMYbAGtDtEh/Pu2Nmw8fIbfd6SxdFcGRzLPsunwGTYdPsO7y/YB0LiOP+3rBdOufgitIgNpEh5AeKBZHnrcgNXDljEKISqLCLLwZJ+mPNmnKQdP5fP7jjT+2JFG0tEsDp7K5+CpfOYkHgFsmz20rx9Mu3rBtIkKoml4AA1q++Ely+DFVXAsY5SaXUIIN3FFo+KMGTNo1KgRFouF2NhY1q9ff8nj58+fT8uWLbFYLLRr145ff/210uuKojBx4kTq1q2Lr68v8fHx7N2790qa5nIcRYnl+UYIj+ZlMtK9cSgv39qGVWP7snpsX968uz13dKpHvbIlLAdO5rMw6QSv/byTBz9OJPaNpbR75Q9uf28Nz32bxH+X7GHehqOs3nuSfRl5nC0q0bhXNT9euCtHRQEZC4TweDFh/ozs3YQFT/Yk6eWb+GRoFx7rGUPrukGYjAZO5RWyLCWD/y3dy/CvNnH9tJW0mriY+LdXMuKrjUz6bRdfJhziz53p7DiRTdbZIlypaskvv/xCbGwsvr6+1KpVi0GDBlV6/ciRI9x88834+fkRHh7OCy+8QEnJpce7zMxMBg8eTFBQECEhIQwbNoy8vMqbAmzbto1rr70Wi8VCdHQ0b775Zk13TdfSyjYLiZTMLiGEm6h2Zte3337Lc889x8yZM4mNjeWdd96hX79+7N69m/Dw8AuOX7t2LQ888ACTJk3illtuYc6cOQwaNIjNmzfTtm1bAN58802mT5/OF198QUxMDBMmTKBfv37s3LkTi8W9L7ieVqBeCHF5omv7EV3bj3u7RANwOq+Q7cez2XbM9rX/ZB6HT+eTV1jC1mPZbD2WXeX7hPh5ExZgJtTfx/bfAB9C/c2M6N0Yi7fJqX1wxnjhriSzSwhRlSCLNze0stXuAigoKmVnag7bj2Wx7Vg2KWm5HDiVx7liK/sy8tiXUfWujn4+JsIDzYSWjQf2sWBAu0jaRAWr2aVL+v7773n88cd54403uP766ykpKSE5OdnxemlpKTfffDORkZGsXbuW1NRUHn74Yby9vXnjjTcu+r6DBw8mNTWVJUuWUFxczKOPPsrw4cOZM2cOYKv/ctNNNxEfH8/MmTPZvn07jz32GCEhIQwfPtzp/dZafmEJGWW7Mdav5TmbJwkh3Fu1C9THxsbStWtX3nvvPQCsVivR0dE89dRTjBs37oLj77vvPvLz8/n5558d3+vevTsdO3Zk5syZKIpCVFQUzz//PP/85z8ByM7OJiIigs8//5z777//b9t0tQUh92XkUlBkrfbP1YRvNhxhTuIRHoxtwBt3tNOkDUII11RYUsqR02fZfzKP/SfzOXbmLCeyznEiq4DU7HPkFV58pnvf6wOuaMlLda63NT1e1HT7zncyt9Axs622o2fO8uTszYT4eZM08SZN2iCEcE1Wq0Jqzjn2Z+SVTYScJTW7gBNZ50jNLuBUXtFFf3b6A524rUNUtc/pjGLsJSUlNGrUiH//+98MGzasymN+++03brnlFk6cOEFEhC0AOHPmTF588UVOnjyJj4/PBT+za9cuWrduzYYNG+jSpQsAixcvZuDAgRw7doyoqCg++OADXnrpJdLS0hzvMW7cOBYuXEhKSspltf9qfycpaTkUl2iTgbf5yBle/nEHdQLNbHgpXpM2CCHE5XJKgfqioiI2bdrE+PHjHd8zGo3Ex8eTkJBQ5c8kJCTw3HPPVfpev379WLhwIQAHDx4kLS2N+PjyC2twcDCxsbEkJCRUGewqLCyksLDQ8f9zcnKq040LPD9/G1uPZl3Ve1wto0zmCyGqyexlollEIM0iAqt8PedcMalZ5zidV8ip/CJO5xVyOq+IvMISp9d2ccZ4UZWaHA8WbjnO67/uuuKfrwmS2SWEqC6j0UC9EF/qhfhyXfM6F7x+rriUtOxznMwrtI0HeUWczividH4hzSMCNGhx1TZv3szx48cxGo106tSJtLQ0OnbsyNSpUx3ZvQkJCbRr184R6ALbOPHEE0+wY8cOOnXqdMH7JiQkEBIS4gh0AcTHx2M0GklMTOSOO+4gISGB6667rlKwrF+/fkyZMoUzZ85Qq1atC963pp9HHv9yI0czC67qPa5Wl4YX9lMIIVxVtYJdp06dorS0tNIAAxAREXHRWY+0tLQqj09LS3O8bv/exY4536RJk/j3v/9dnaZfUp0AH6I0XJ/u62PilvbVn1UTQohLCbJ4ExTpDVQdDHMmZ4wXVanJ8cDf7KXpWABwd9myVSGEqCkWbxONwvxpFOavdVMu6cCBAwC88sorvP322zRq1Ihp06bRp08f9uzZQ+3atS86TgAXHSvS0tIuWDrv5eXleD/7MTExMRd936qCXTX9PBIRaKG0VLvaan5mLx6/rrFm5xdCiJrmkrsxjh8/vtLsf05ODtHRV/6A8PHQrjXRLCGEECqryfHgwdgGPBjboKaaJoQQAttywClTplzymF27dmG12kqKvPTSS9x1110AfPbZZ9SvX5/58+czYsQIp7e1Omr6eeS7J3rURLOEEEKUqVawKywsDJPJRHp6eqXvp6enExkZWeXPREZGXvJ4+3/T09OpW7dupWM6duxY5XuazWbMZtkWVwgh9MoZ40VVZDwQQgh9e/7553nkkUcueUzjxo1JTU0FoHXr1o7vm81mGjduzJEjRwDbOHH+rr72ceNSY0tGRkal75WUlJCZmVnpeaSq8edS7yvjjxBC6Fu1irb4+PjQuXNnli5d6vie1Wpl6dKlxMXFVfkzcXFxlY4HWLJkieP4mJgYIiMjKx2Tk5NDYmLiRd9TCCGEvjljvBBCCOF66tSpQ8uWLS/5ZR8zzGYzu3fvdvxscXExhw4domHDhoBtnNi+fXul4NWSJUsICgqqFCSrKC4ujqysLDZt2uT43rJly7BarcTGxjqOWbVqFcXFxZXet0WLFlUuYRRCCOEClGqaO3euYjablc8//1zZuXOnMnz4cCUkJERJS0tTFEVRhgwZoowbN85x/F9//aV4eXkpb731lrJr1y7l5ZdfVry9vZXt27c7jpk8ebISEhKiLFq0SNm2bZty++23KzExMUpBQcFltSk7O1sBlOzs7Op2RwghRDVU53rrjPGiJtsnhBDiyjjrWjtmzBilXr16yu+//66kpKQow4YNU8LDw5XMzExFURSlpKREadu2rXLTTTcpSUlJyuLFi5U6deoo48ePd7xHYmKi0qJFC+XYsWOO7/Xv31/p1KmTkpiYqKxZs0Zp1qyZ8sADDzhez8rKUiIiIpQhQ4YoycnJyty5cxU/Pz/lww8/vOy2y/gjhBDquNzrbbVrdt13332cPHmSiRMnOnZJWbx4saOI45EjRzAayxPGevTowZw5c/i///s//vWvf9GsWTMWLlzo2FUFYOzYseTn5zN8+HCysrLo1asXixcvxmLRtlCwEEKIK+eM8UIIIYT7mjp1Kl5eXgwZMoSCggJiY2NZtmyZI7vKZDLx888/88QTTxAXF4e/vz9Dhw7l1VdfdbzH2bNn2b17d6UsrdmzZzN69GhuuOEGjEYjd911F9OnT3e8HhwczB9//MGoUaPo3LkzYWFhTJw4keHDh6vXeSGEEDXKoCiKdtt+1JCcnByCg4PJzs4mKChI6+YIIYTb0vv1Vu/tE0IIdyDX2gvJ70QIIdRxudfbatXsEkIIIYQQQgghhBBCzyTYJYQQQgghhBBCCCHchgS7hBBCCCGEEEIIIYTbkGCXEEIIIYQQQgghhHAbEuwSQgghhBBCCCGEEG7DS+sG1AT7hpI5OTkat0QIIdyb/Tqr1418ZTwQQgjn0/tYoAUZf4QQQh2XOwa5RbArNzcXgOjoaI1bIoQQniE3N5fg4GCtm3EBGQ+EEEI9eh0LtCDjjxBCqOvvxiCD4gZTMlarlRMnThAYGIjBYKj2z+fk5BAdHc3Ro0cJCgpyQgv1y5P7Dp7df+m79P1K+q4oCrm5uURFRWE06m8lvIwHV076Ln2XvnuWq+m/3scCLcj4c+U8ue/g2f2Xvkvfnfk84haZXUajkfr161/1+wQFBXncB83Ok/sOnt1/6bv0vbr0PIsv48HVk75L3z2NJ/cdrrz/eh4LtCDjz9Xz5L6DZ/df+i59r67LGYNkKkYIIYQQQgghhBBCuA0JdgkhhBBCCCGEEEIItyHBLsBsNvPyyy9jNpu1borqPLnv4Nn9l75L38WFPPn3I32XvnsaT+47SP/1xpP/PTy57+DZ/Ze+S9+dyS0K1AshhBBCCCGEEEIIAZLZJYQQQgghhBBCCCHciAS7hBBCCCGEEEIIIYTbkGCXEEIIIYQQQgghhHAbEuwSQgghhBBCCCGEEG5Dgl1CCCGEEEIIIYQQwm1IsAuYMWMGjRo1wmKxEBsby/r167VuUo2bNGkSXbt2JTAwkPDwcAYNGsTu3bsrHXPu3DlGjRpFaGgoAQEB3HXXXaSnp2vUYueZPHkyBoOBZ555xvE9d+778ePHeeihhwgNDcXX15d27dqxceNGx+uKojBx4kTq1q2Lr68v8fHx7N27V8MW14zS0lImTJhATEwMvr6+NGnShNdee42KG9C6U99XrVrFrbfeSlRUFAaDgYULF1Z6/XL6mpmZyeDBgwkKCiIkJIRhw4aRl5enYi+0JWOBjTtfDyuSscAzxgLwrPFAxgLXJWOQjTtfhyuSMcgzxiBPGn9Ah2OQ4uHmzp2r+Pj4KJ9++qmyY8cO5fHHH1dCQkKU9PR0rZtWo/r166d89tlnSnJyspKUlKQMHDhQadCggZKXl+c4ZuTIkUp0dLSydOlSZePGjUr37t2VHj16aNjqmrd+/XqlUaNGSvv27ZUxY8Y4vu+ufc/MzFQaNmyoPPLII0piYqJy4MAB5ffff1f27dvnOGby5MlKcHCwsnDhQmXr1q3KbbfdpsTExCgFBQUatvzqvf7660poaKjy888/KwcPHlTmz5+vBAQEKP/73/8cx7hT33/99VflpZdeUhYsWKAAyg8//FDp9cvpa//+/ZUOHToo69atU1avXq00bdpUeeCBB1TuiTZkLJCxQFHct++ePBYoimeNBzIWuCYZg2QMUhT37bsnj0GeNP4oiv7GII8PdnXr1k0ZNWqU4/+XlpYqUVFRyqRJkzRslfNlZGQogLJy5UpFURQlKytL8fb2VubPn+84ZteuXQqgJCQkaNXMGpWbm6s0a9ZMWbJkidK7d2/H4OLOfX/xxReVXr16XfR1q9WqREZGKlOnTnV8LysrSzGbzco333yjRhOd5uabb1Yee+yxSt+78847lcGDByuK4t59P39wuZy+7ty5UwGUDRs2OI757bffFIPBoBw/fly1tmtFxgIZC9y57548FiiK544HMha4DhmDZAxy57578hjkqeOPouhjDPLoZYxFRUVs2rSJ+Ph4x/eMRiPx8fEkJCRo2DLny87OBqB27doAbNq0ieLi4kq/i5YtW9KgQQO3+V2MGjWKm2++uVIfwb37/uOPP9KlSxfuuecewsPD6dSpEx999JHj9YMHD5KWllap78HBwcTGxrp833v06MHSpUvZs2cPAFu3bmXNmjUMGDAAcO++n+9y+pqQkEBISAhdunRxHBMfH4/RaCQxMVH1NqtJxgIZC8C9++7JYwHIeGAnY4E+yRgkYxC4d989eQyS8aecFmOQ19U323WdOnWK0tJSIiIiKn0/IiKClJQUjVrlfFarlWeeeYaePXvStm1bANLS0vDx8SEkJKTSsREREaSlpWnQypo1d+5cNm/ezIYNGy54zZ37fuDAAT744AOee+45/vWvf7FhwwaefvppfHx8GDp0qKN/Vf0NuHrfx40bR05ODi1btsRkMlFaWsrrr7/O4MGDAdy67+e7nL6mpaURHh5e6XUvLy9q167tdr+P88lYIGMBuHffPXksABkP7GQs0CcZg2QMAvfuuyePQTL+lNNiDPLoYJenGjVqFMnJyaxZs0brpqji6NGjjBkzhiVLlmCxWLRujqqsVitdunThjTfeAKBTp04kJyczc+ZMhg4dqnHrnGvevHnMnj2bOXPm0KZNG5KSknjmmWeIiopy+74LcTlkLPAcnjwWgIwHQuiRjEGew5PHIBl/tOXRyxjDwsIwmUwX7HKRnp5OZGSkRq1yrtGjR/Pzzz+zfPly6tev7/h+ZGQkRUVFZGVlVTreHX4XmzZtIiMjg2uuuQYvLy+8vLxYuXIl06dPx8vLi4iICLfte926dWndunWl77Vq1YojR44AOPrnjn8DL7zwAuPGjeP++++nXbt2DBkyhGeffZZJkyYB7t33811OXyMjI8nIyKj0eklJCZmZmW73+zifjAUyFshY4N7XQxkPbGQs0CcZg2QMkjHIfa/BMv6U02IM8uhgl4+PD507d2bp0qWO71mtVpYuXUpcXJyGLat5iqIwevRofvjhB5YtW0ZMTEyl1zt37oy3t3el38Xu3bs5cuSIy/8ubrjhBrZv305SUpLjq0uXLgwePNjxv9217z179rxgS+c9e/bQsGFDAGJiYoiMjKzU95ycHBITE12+72fPnsVorHyJM5lMWK1WwL37fr7L6WtcXBxZWVls2rTJccyyZcuwWq3Exsaq3mY1yVhQTsYC9+y7J48FIOOBnYwF+iRjUDkZg9yz7548Bsn4U06TMegKi+u7jblz5ypms1n5/PPPlZ07dyrDhw9XQkJClLS0NK2bVqOeeOIJJTg4WFmxYoWSmprq+Dp79qzjmJEjRyoNGjRQli1bpmzcuFGJi4tT4uLiNGy181Tc/URR3Lfv69evV7y8vJTXX39d2bt3rzJ79mzFz89P+frrrx3HTJ48WQkJCVEWLVqkbNu2Tbn99ttddrvbioYOHarUq1fPsdXvggULlLCwMGXs2LGOY9yp77m5ucqWLVuULVu2KIDy9ttvK1u2bFEOHz6sKMrl9bV///5Kp06dlMTERGXNmjVKs2bNPGa7eRkLZCxQFPftuyePBYriWeOBjAWuScYgGYMUxX377sljkCeNP4qivzHI44NdiqIo7777rtKgQQPFx8dH6datm7Ju3Tqtm1TjgCq/PvvsM8cxBQUFypNPPqnUqlVL8fPzU+644w4lNTVVu0Y70fmDizv3/aefflLatm2rmM1mpWXLlsqsWbMqvW61WpUJEyYoERERitlsVm644QZl9+7dGrW25uTk5ChjxoxRGjRooFgsFqVx48bKSy+9pBQWFjqOcae+L1++vMq/8aFDhyqKcnl9PX36tPLAAw8oAQEBSlBQkPLoo48qubm5GvRGGzIW2Ljz9fB8MhaUc6fr4fk8aTyQscB1yRhk487X4fPJGFTOXa7B5/Ok8UdR9DcGGRRFUaqfDyaEEEIIIYQQQgghhP54dM0uIYQQQgghhBBCCOFeJNglhBBCCCGEEEIIIdyGBLuEEEIIIYQQQgghhNuQYJcQQgghhBBCCCGEcBsS7BJCCCGEEEIIIYQQbkOCXUIIIYQQQgghhBDCbUiwSwghhBBCCCGEEEK4DQl2CSGEEEIIIYQQQgi3IcEuIYQQQgghhBBCCOE2JNglhBBCCCGEEEIIIdyGBLuEEEIIIYQQQgghhNv4f+SvoXLrLGflAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "id": "7c8208fba8bb0f22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-25T12:41:36.348920Z",
     "start_time": "2023-08-25T12:41:36.250301200Z"
    },
    "collapsed": false
   },
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "243.07px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
